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Polycystic Ovarian Syndrome (PCOS)

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Overview:

Polycystic Ovarian Syndrome (PCOS) stands as one of the most prevalent endocrine disorders affecting reproductive aged women globally. This complex condition disrupts hormone levels within the body, impacting the ovaries and often leading to a myriad of symptoms. PCOS is characterized by a spectrum of irregularities, including menstrual irregularities, hormonal imbalances, and the formation of cysts on the ovaries. Its manifestation varies among individuals, typically marked by excessive production of androgens, irregular menstrual cycles, and difficulties in ovulation, potentially leading to fertility challenges. Beyond reproductive health concerns, PCOS also presents with metabolic implications, such as insulin resistance, which can elevate the risk of developing type 2 diabetes and cardiovascular issues. The exact cause of PCOS remains multifaceted and isn’t singularly defined, encompassing a combination of genetic, hormonal, and lifestyle factors. As a chronic disorder, PCOS poses significant challenges to those affected, impacting their overall health and quality of life.

This syndrome’s diagnosis is often challenging due to its diverse array of symptoms and the absence of a singular definitive test. Clinicians typically rely on a set of diagnostic criteria, such as irregular menstrual cycles, elevated androgen levels, and the presence of ovarian cysts, to identify and evaluate PCOS. However, the complexity of this condition extends beyond its diagnostic challenges, as managing PCOS necessitates a multifaceted approach. Treatment strategies often focus on alleviating specific symptoms, such as irregular periods, infertility, and excessive hair growth, through medications, lifestyle modifications, and sometimes surgical interventions. Additionally, lifestyle changes involving diet adjustments and regular exercise play a crucial role in managing the metabolic aspects of PCOS. Research into PCOS continues to expand, aiming to enhance diagnostic accuracy, refine treatment approaches, and deepen our understanding of its underlying mechanisms to better support individuals grappling with this pervasive syndrome.

Hormones that are involved in PCOS are:

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PCOS Symptoms:

Many of these symptoms can be attributed to other causes or go unnoticed but it is very common for PCOS to go undiagnosed for some time. Here are some symptoms that help with the diagnosis:

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Other symptoms may include:

It’s good to note that not everyone who is diagnosed with PCOS experiences all of these symptoms and should always consult with a their PCP or OBGYN to get an accurate diagnosis.

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The Problem:

Early identification of risk factors associated with PCOS can assist in timely interventions and lifestyle adjustments. Tailoring suggestions or treatments based on individualized risk profiles has the potential to enhance patient outcomes. Employing predictive models can play a pivotal role in increasing awareness regarding PCOS risk factors and preventive measures. However, limitations may exist in accessing comprehensive and varied datasets containing accurate demographic, clinical, and lifestyle data. It is crucial to ensure the model’s transparency and interpretability to facilitate well-informed decisions and recommendations. Additionally, it’s imperative that the model demonstrates proficiency across diverse demographic groups and populations. Addressing these challenges involves the development of a robust predictive model using machine learning techniques. Such an endeavor holds the promise of significantly contributing to the identification of individuals at risk of PCOS, thereby enabling early interventions and guiding personalized healthcare strategies for improved management of the condition. This project will investigate publicly available datasets that will be used to develop machine learning models that predicts the probability or risk of an individual having or developing PCOS based on demographic information (age, ethnicity, geographical location), clinical data (hormonal levels, BMI, menstrual irregularities), and lifestyle factors (dietary habits, exercise routine, stress levels).

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The Data:

The Polycystic ovary syndrome (PCOS) dataset, available on Kaggle.com, is comprised of two csv files labeled PCOS_data_without_infertility and PCOS_infertility. In total, these files encompass 48 variables and 541 data entries all collected from 10 different hospitals across Kerala, India. The dataset contains all physical and clinical parameters to determine PCOS and infertility related issues.

Full description of the variables below:

Variables Description
“Sl..No” unique identification number assigned to each entry
“Patient.File.No.” file number for each patient’s record.
“PCOS..Y.N.” presence or absence of PCOS
“I…beta.HCG.mIU.mL.” pregnancy hormone case I measured in
milli-international units per liter (mIU/L)
“II….beta.HCG.mIU.mL.” pregnancy hormone case II measured in
milli-international units per liter (mIU/L)
“AMH.ng.mL.” detects ovarian reserve (egg count)
“Age..yrs.” age of patient in years
“Weight..Kg.” weight of patient in kg
“Height.Cm.” height of patient in cm
“BMI” body mass index
“Blood.Group” Blood Groups:
A+ = 11, A- = 12, B+ = 13, B- = 14,
O+ = 15, O- = 16, AB+ = 17, AB- = 18
“Pulse.rate.bpm.” beats per minute
“RR..breaths.min.” respiration rates per minute
“Hb.g.dl.” hemoglobin concentration measured in
grams per deciliter (g/dL).
“Cycle.R.I.” cycle Regularity Index used to assess the
regularity or irregularity of menstrual cycles
in women: 4 indicates irregular menstrual cycle,
2 indicates a regular menstrual cycle
“Cycle.length.days.” length of menstrual cycle
“Marraige.Status..Yrs.” years married
“Pregnant.Y.N.” pregnant yes or no
“No..of.aborptions” number of abortions
“FSH.mIU.mL.” follicle stimulating hormone measured
in milli-international units per liter (mIU/L)
“LH.mIU.mL.” luteinizing hormone (increases during ovulation)
measured in milli-international units per liter (mIU/L)
“FSH.LH” ratio between Follicle-Stimulating Hormone (FSH)
and Luteinizing Hormone (LH)
“Hip.inch.” measurement of hips in inches
“Waist.inch.” measurement of waist in inches
“Waist.Hip.Ratio” ratio of measurement of waist and hip
“TSH..mIU.L.” thyroid stimulating hormone measured in
milli-international units per liter (mIU/L)
“AMH.ng.mL.” Anti-Müllerian Hormone (AMH) measured in
nanograms per milliliter (ng/mL); a marker used in
reproductive medicine to assess ovarian reserve
“PRL.ng.mL.” Prolactin measured in nanograms per milliliter (ng/mL);
a hormone produced by the pituitary gland
“Vit.D3..ng.mL.” Vitamin D3 measured in nanograms per milliliter (ng/mL);
is essential for bone health, immune function, and
various other bodily processes.
“PRG.ng.mL.” Progesterone measured in nanograms per milliliter (ng/mL); a
hormone involved in the menstrual cycle, pregnancy, and
maintaining the uterine lining for a developing embryo.
“RBS.mg.dl.” Random Blood Sugar measured in milligrams per deciliter
(mg/dL); it represents the level of glucose (sugar) present
in the blood at a random time, without fasting.
“Weight.gain.Y.N.” weight gain yes or no
“hair.growth.Y.N.” hair growth yes or no (hirsutism)
“Skin.darkening..Y.N.” darkening of skin yes or no
“Hair.loss.Y.N.” hair loss yes or no
“Pimples.Y.N.” pimples (acne) yes or no
“Fast.food..Y.N.” consumption of fast food yes or no
“Reg.Exercise.Y.N.” regularly exercise yes or no
“BP._Systolic..mmHg.” systolic blood pressure measured in millimeters of mercury (mmHg)
“BP._Diastolic..mmHg.” diastolic blood pressure measured in millimeters of mercury (mmHg)
“Follicle.No…L.” number of follicles on left ovary
“Follicle.No…R.” number of follicles in right ovary
“Avg..F.size..L…mm.” average size of follicles in left ovary measured in millimeters (mm)
“Avg..F.size..R…mm.” average size of follicles in right ovary measured in millimeters (mm)
“Endometrium..mm.” size of the endometrial thickness in millimeters (mm)

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Load Libraries:

The following libraries were utilized in this assignment:

# load libraries
library(tidyverse) # data prep
library(DataExplorer) # histograms for datasets
library(skimr) # data prep
library(rpart) # decision tree package
library(rpart.plot) # decision tree display package
library(kableExtra) # kable function for tables 
library(knitr) # kable function for table
library(tidyr) # splitting data
library(ggplot2) # graphing
library(hrbrthemes) # chart customization
library(gridExtra) # layering charts
library(stringr) # data prep
library(tidymodels) # predictions
library(corrplot) # correlation plot
library(randomForest) # for the random forest
library(caret) # confusion matrix
library("e1071") #svm
library(formattable) 
library(corrplot) # correlation plot
library(caret) # confusion matrix
library(neuralnet) # neural network
library(stats) # linear and logistic regression
library(gbm) # generalized boosted models
library(xgboost) # extreme gradient boosting
library(kknn) # weighted k-Nearest neighbors
library(jtools)  # use of summ()
library(patchwork) # ggplot2 multiplot title
library(class) # knn function

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Load data:

The dataset selected has been incorporated into my GitHub and imported into R.

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Displaying the pcos data:

Table 1.pcos data
Sl..No Patient.File.No. PCOS..Y.N. I…beta.HCG.mIU.mL. II….beta.HCG.mIU.mL. AMH.ng.mL.
1 10001 0 1.990 1.990000e+00 2.07
2 10002 0 60.800 1.990000e+00 1.53
3 10003 1 494.080 4.940800e+02 6.63
4 10004 0 1.990 1.990000e+00 1.22
5 10005 0 801.450 8.014500e+02 2.26
6 10006 0 237.970 1.990000e+00 6.74
7 10007 0 1.990 1.990000e+00 3.05
8 10008 0 100.510 1.005100e+02 1.54
9 10009 0 1.990 1.990000e+00 1
10 10010 0 1.990 1.990000e+00 1.61
11 10011 0 158.510 1.585100e+02 4.47
12 10012 0 1.990 1.990000e+00 1.67
13 10013 1 1214.230 1.214230e+03 7.94
14 10014 0 1.990 1.990000e+00 2.38
15 10015 0 1.990 1.990000e+00 0.88
16 10016 0 1.990 1.990000e+00 0.69
17 10017 0 8104.210 9.155000e+01 3.78
18 10018 0 1.990 1.990000e+00 1.92
19 10019 0 1.990 1.990000e+00 1
20 10020 1 23.580 1.990000e+00 2.07
21 10021 0 1.990 1.990000e+00 2.85
22 10022 0 749.980 7.499800e+02 2.13
23 10023 0 218.650 2.186500e+02 4.13
24 10024 0 13.000 1.300000e+01 2.5
25 10025 1 610.630 6.106300e+02 1.89
26 10026 0 4490.180 4.490180e+03 0.26
27 10027 1 1.990 1.990000e+00 3.84
28 10028 0 689.580 1.124000e+01 2.5
29 10029 0 15.000 1.500000e+01 3.56
30 10030 1 768.030 7.680300e+02 1.56
31 10031 0 612.320 1.200000e+01 1.69
32 10032 1 10.000 1.000000e+01 1.89
33 10033 0 1.990 1.990000e+00 2.34
34 10034 0 20.000 2.000000e+01 1.58
35 10035 0 1277.490 3.066000e+01 2.36
36 10036 0 1455.000 1.455000e+03 3.64
37 10037 0 18.000 1.990000e+00 2.78
38 10038 0 12.000 1.990000e+00 0.88
39 10039 0 10.000 1.990000e+00 2.36
40 10040 0 1.990 1.990000e+00 0.33
41 10041 0 10.000 1.990000e+00 2.35
42 10042 0 497.410 4.974100e+02 3.88
43 10043 0 10.000 1.990000e+00 3.55
44 10044 0 161.147 1.670000e+02 4.33
45 10045 1 1305.350 1.990000e+00 3.55
46 10046 0 43.500 9.830000e+00 2.36
47 10047 1 141.060 1.410600e+02 3.66
48 10048 0 1500.000 5.285000e+02 4.33
49 10049 0 10.000 1.990000e+00 1
50 10050 1 10.000 1.990000e+00 4.5
51 10051 1 77.600 1.990000e+00 3.2
52 10052 0 177.570 1.775700e+02 2.1
53 10053 1 160.770 1.000000e+01 4.5
54 10054 0 180.330 1.200000e+01 6.55
55 10055 0 1.990 1.990000e+00 1.2
56 10056 0 274.700 1.500000e+01 2.33
57 10057 0 1.990 1.990000e+00 3.22
58 10058 0 249.870 1.200000e+01 2.333
59 10059 0 1.990 1.990000e+00 2.31
60 10060 0 1.990 1.990000e+00 2.36
61 10061 0 626.920 6.503000e+01 4.2
62 10062 1 8.000 1.990000e+00 3.21
63 10063 0 1.990 1.990000e+00 2.14
64 10064 0 757.170 2.000000e+01 2.3
65 10065 0 1.990 1.990000e+00 4.6
66 10066 0 10.000 1.990000e+00 2.3
67 10067 0 31.330 1.990000e+00 5.8
68 10068 0 218.590 1.990000e+00 5.2
69 10069 1 218.590 1.990000e+00 2.14
70 10070 1 173.660 1.736600e+02 4.63
71 10071 0 4.420 1.990000e+00 1.01
72 10072 0 15.000 1.990000e+00 2.58
73 10073 0 161.490 1.614900e+02 5.8
74 10074 0 1.990 1.990000e+00 0.35
75 10075 0 603.810 1.500000e+01 5.23
76 10076 0 486.390 3.980000e+00 3.68
77 10077 1 756.610 7.566100e+02 2.14
78 10078 0 1.990 1.990000e+00 2.55
79 10079 0 1.990 1.990000e+00 4.91
80 10080 0 10.000 1.990000e+00 1.03
81 10081 0 1.990 1.990000e+00 6.56
82 10082 0 255.020 2.550200e+02 3.91
83 10083 0 1.990 1.990000e+00 5.42
84 10084 0 1.990 1.990000e+00 1.65
85 10085 0 1.990 1.990000e+00 2.06
86 10086 0 273.700 2.737000e+02 1.81
87 10087 0 1.990 1.990000e+00 3.81
88 10088 0 162.050 1.990000e+00 2.26
89 10089 1 1.990 1.990000e+00 3.65
90 10090 0 1.990 1.990000e+00 8.98
91 10091 0 1.990 1.990000e+00 1.7
92 10092 0 1.990 1.990000e+00 3.18
93 10093 0 1.990 1.990000e+00 2.75
94 10094 0 1.990 1.990000e+00 0.86
95 10095 0 1.990 1.990000e+00 2.29
96 10096 1 1247.560 1.500000e+01 2.19
97 10097 1 15.000 1.990000e+00 8.46
98 10098 1 320.560 1.446000e+01 4.59
99 10099 0 145.890 1.458900e+02 1.04
100 10100 0 5.030 1.990000e+00 4.27
101 10101 0 100.090 1.000900e+02 3.86
102 10102 0 1.990 1.990000e+00 1.42
103 10103 1 110.170 1.101700e+02 10.07
104 10104 0 174.370 1.743700e+02 0.98
105 10105 0 5202.590 7.551000e+01 4.07
106 10106 1 600.180 1.990000e+00 3.2
107 10107 0 1.990 1.990000e+00 3.9
108 10108 1 120.960 1.990000e+00 10
109 10109 1 397.080 3.893060e+03 16.9
110 10110 0 926.240 6.002300e+02 17
111 10111 0 26290.260 3.350190e+03 21.9
112 10112 0 32460.970 9.763000e+01 1.6
113 10113 0 14.470 1.440000e+01 3.3
114 10114 1 332.510 2.000000e+00 21
115 10115 1 70.170 2.000000e+00 12.7
116 10116 0 177.570 1.775800e+02 1.8
117 10117 0 3.100 1.217000e+01 3.6
118 10118 1 59.390 2.000000e+00 15
119 10119 0 100.680 2.530000e+01 5
120 10120 0 566.240 1.002000e+02 3.3
121 10121 0 1.990 4.796600e+02 3.3
122 10122 0 357.090 1.900000e+00 3.9
123 10123 1 138.680 5.860600e+02 17.9
124 10124 1 1.990 1.104167e-01 19.8
125 10125 1 1.990 1.990000e+00 9.2
126 10126 0 363.130 2.800000e+00 2.4
127 10127 0 84.790 1.990000e+00 4.5
128 10128 0 1.990 1.536000e+01 5.14
129 10129 0 98.910 5.408000e+01 2.4
130 10130 0 1.990 1.990000e+00 0.3
131 10131 1 152.130 1.521300e+02 11.48
132 10132 0 1.990 1.990000e+00 19.3
133 10133 1 1.990 1.990000e+00 8.8
134 10134 1 342.160 1.990000e+00 19
135 10135 0 3.050 3.050000e+00 4.3
136 10136 0 1.990 1.990000e+00 1.4
137 10137 0 201.360 2.013600e+02 12.6
138 10138 0 1.990 1.990000e+00 4.8
139 10139 0 21977.290 1.606969e+04 4.6
140 10140 0 1.990 1.990000e+00 17.1
141 10141 0 3.410 5.708000e+01 2.1
142 10142 1 1.990 1.990000e+00 11.6
143 10143 1 1.990 1.990000e+00 18.4
144 10144 0 1.990 1.990000e+00 1.8
145 10145 0 1.990 1.990000e+00 9.9
146 10146 0 1.990 1.990000e+00 3.7
147 10147 0 1.990 1.990000e+00 2.9
148 10148 0 1.990 1.990000e+00 2
149 10149 1 1.990 1.990000e+00 4
150 10150 0 1.990 1.990000e+00 1.6
151 10151 1 232.710 2.327100e+02 15.9
152 10152 1 1.990 1.990000e+00 7.51
153 10153 1 1.990 1.990000e+00 10.04
154 10154 1 1.990 1.990000e+00 6.86
155 10155 0 60.800 2.350000e+01 7.02
156 10156 0 1.990 1.990000e+00 8.75
157 10157 0 1.990 1.990000e+00 5.27
158 10158 0 289.360 1.803000e+02 1.4
159 10159 1 1.990 1.990000e+00 9
160 10160 0 366.800 1.023000e+02 3.56
161 10161 0 96.200 1.990000e+00 3.41
162 10162 0 1.990 1.990000e+00 0.45
163 10163 1 1.990 1.990000e+00 2.53
164 10164 0 1.990 1.990000e+00 0.29
165 10165 0 481.300 4.813000e+02 2.6
166 10166 0 1.990 1.990000e+00 2.83
167 10167 0 563.800 5.638000e+02 1.89
168 10168 1 2.590 1.990000e+00 2.01
169 10169 1 1.990 1.990000e+00 2.83
170 10170 1 4.360 4.320000e+00 5.67
171 10171 0 833.500 2.305000e+02 1.68
172 10172 1 1.990 1.990000e+00 3.65
173 10173 0 2036.200 1.553000e+02 3.63
174 10174 1 3.888 3.888000e+00 3.49
175 10175 0 698.200 5.236000e+02 2.01
176 10176 1 1.990 1.990000e+00 8
177 10177 1 3.660 1.650000e+00 9
178 10178 1 1.990 1.990000e+00 11.48
179 10179 1 3.830 3.830000e+00 10.25
180 10180 0 2045.300 5.691000e+02 2.36
181 10181 1 1.990 1.990000e+00 32
182 10182 0 1.300 1.990000e+00 3.38
183 10183 0 1.990 1.990000e+00 1.35
184 10184 0 323.000 2.365000e+02 2.38
185 10185 0 896.600 8.966000e+02 5.78
186 10186 0 1.990 1.990000e+00 4.66
187 10187 1 2.580 2.580000e+00 1.99
188 10188 0 569.300 5.693000e+02 1.28
189 10189 0 108.660 1.086600e+02 3.99
190 10190 1 1.990 1.990000e+00 5.69
191 10191 0 1.990 1.990000e+00 7.81
192 10192 1 3.990 3.990000e+00 6.41
193 10193 0 455.800 1.218000e+02 5.76
194 10194 0 2.010 1.990000e+00 1.68
195 10195 0 122.580 1.225800e+02 8.75
196 10196 1 1.990 1.990000e+00 6.65
197 10197 1 2.100 1.990000e+00 4.15
198 10198 0 1.990 1.990000e+00 1.86
199 10199 0 689.100 3.552800e+02 2.04
200 10200 1 1.990 1.990000e+00 7.25
201 10201 0 3.440 1.990000e+00 1.04
202 10202 0 122.300 1.223000e+02 1.91
203 10203 0 596.200 5.962000e+02 2.3
204 10204 1 1.990 1.990000e+00 5.61
205 10205 0 4.600 2.800000e+00 3.02
206 10206 1 2.100 1.990000e+00 5.25
207 10207 1 2.500 1.990000e+00 2.38
208 10208 0 2054.300 5.887000e+02 7
209 10209 0 147.600 1.476000e+02 3.17
210 10210 1 1.990 1.990000e+00 5.57
211 10211 1 2.200 1.990000e+00 4.57
212 10212 1 12.370 1.237000e+01 0.37
213 10213 0 1.990 1.990000e+00 16.9
214 10214 1 144.630 1.446300e+02 26.4
215 10215 0 25000.000 4.750400e+02 5.96
216 10216 0 1.990 1.990000e+00 9.1
217 10217 1 515.530 5.155300e+02 6.6
218 10218 1 1.990 9.969000e+01 22
219 10219 0 70.420 7.042000e+01 1.9
220 10220 0 342.910 3.429100e+02 4.3
221 10221 0 1.990 1.990000e+00 0.37
222 10222 1 1.990 1.990000e+00 17.6
223 10223 1 148.520 1.485200e+02 1.1
224 10224 0 1.990 1.990000e+00 7.8
225 10225 0 1.990 1.990000e+00 2.9
226 10226 0 272.780 2.727800e+02 7.7
227 10227 0 355.510 3.555100e+02 9.7
228 10228 1 1.990 1.990000e+00 0.2
229 10229 0 1.990 1.990000e+00 2.5
230 10230 0 1.990 1.990000e+00 12
231 10231 1 1.990 1.990000e+00 1.4
232 10232 0 150.910 1.509100e+02 16.7
233 10233 1 451.960 1.990000e+00 13.6
234 10234 0 392.730 1.990000e+00 16.8
235 10235 0 391.460 3.914600e+02 3.5
236 10236 0 418.820 1.990000e+00 1.3
237 10237 0 1.990 1.990000e+00 3.14
238 10238 1 1.990 1.990000e+00 1.25
239 10239 0 464.120 4.641200e+02 7.7
240 10240 0 389.260 4.177000e+01 7.3
241 10241 1 1390.580 1.390580e+03 7.2
242 10242 0 1.990 1.990000e+00 3.29
243 10243 0 1.990 1.990000e+00 2.69
244 10244 1 213.830 2.138300e+02 2.1
245 10245 1 1.990 1.990000e+00 4.1
246 10246 1 353.600 4.590000e+01 6.2
247 10247 0 30.600 1.836000e+01 14.6
248 10248 1 154.480 1.544800e+02 4.71
249 10249 1 1.990 1.990000e+00 1.25
250 10250 0 1.990 1.990000e+00 1
251 10251 1 50.430 1.990000e+00 11.1
252 10252 0 1.990 1.990000e+00 1.9
253 10253 1 25000.000 2.500000e+04 6.2
254 10254 0 1.990 1.990000e+00 1.9
255 10255 0 41.250 1.990000e+00 1.5
256 10256 0 1.990 6.385200e+02 2.9
257 10257 0 413.620 1.990000e+00 2.25
258 10258 0 1.990 1.990000e+00 6.8
259 10259 0 221.280 1.990000e+00 0.8
260 10260 1 227.360 1.990000e+00 7.21
261 10261 0 1.990 1.990000e+00 4.2
262 10262 0 643.410 1.990000e+00 6.2
263 10263 0 1.990 1.990000e+00 0.9
264 10264 0 3.250 4.760000e+00 16.8
265 10265 0 165.610 1.990000e+00 16.9
266 10266 0 1.990 1.990000e+00 8.5
267 10267 0 92.920 1.990000e+00 3.9
268 10268 1 117.140 1.813000e+01 66
269 10269 0 100.580 1.990000e+00 26.8
270 10270 1 1.990 1.990000e+00 1.15
271 10271 0 127.040 1.990000e+00 4.1
272 10272 0 164.910 1.990000e+00 3.2
273 10273 0 1.990 1.990000e+00 0.16
274 10274 0 785.440 1.990000e+00 8.1
275 10275 0 1.990 1.990000e+00 0.56
276 10276 1 355.080 1.990000e+00 5.3
277 10277 1 89.340 8.934000e+01 1.8
278 10278 1 1.990 1.990000e+00 7.3
279 10279 1 412.900 3.660400e+02 6.5
280 10280 0 184.800 1.990000e+00 21
281 10281 0 1.990 1.990000e+00 0.9
282 10282 0 1.990 1.990000e+00 15.3
283 10283 1 454.270 1.434000e+01 10.6
284 10284 0 3.100 7.562000e+01 4.2
285 10285 0 1.990 1.990000e+00 4.7
286 10286 0 227.560 4.960000e+00 4.8
287 10287 0 1.990 1.990000e+00 1.7
288 10288 1 53.090 1.990000e+00 5.4
289 10289 0 1.990 1.990000e+00 1.2
290 10290 0 213.210 1.990000e+00 5.4
291 10291 1 1.990 1.990000e+00 17.5
292 10292 0 1.990 1.990000e+00 6
293 10293 0 1.990 1.990000e+00 2
294 10294 1 1134.400 1.134400e+03 21.8
295 10295 1 785.950 7.859500e+02 18.5
296 10296 1 291.430 1.990000e+00 12.4
297 10297 0 20.810 1.990000e+00 10.8
298 10298 0 1.990 1.990000e+00 4.5
299 10299 0 1.990 1.990000e+00 4.2
300 10300 0 1.990 1.990000e+00 1.2
301 10301 1 1.990 1.990000e+00 10.8
302 10302 0 110.780 1.990000e+00 10
303 10303 0 1.990 1.990000e+00 18.7
304 10304 1 1.990 1.900000e+00 18
305 10305 0 1.990 1.990000e+00 0.28
306 10306 0 42.000 1.990000e+00 a
307 10307 1 1.990 1.990000e+00 1.03
308 10308 0 229.860 2.298600e+02 3.02
309 10309 1 1.990 1.990000e+00 1
310 10310 1 1.990 1.990000e+00 1.5
311 10311 0 3.900 3.900000e+00 3
312 10312 1 297.210 2.972100e+02 1
313 10313 1 277.280 2.772800e+02 1.06
314 10314 0 1.990 7.833600e+02 3.05
315 10315 1 1.990 1.990000e+00 5.4
316 10316 0 21084.210 2.108421e+04 3.02
317 10317 0 409.850 4.098500e+02 2.23
318 10318 0 17243.970 4.101300e+02 2.06
319 10319 0 479.600 4.796000e+02 3.33
320 10320 0 545.780 5.457800e+02 1
321 10321 1 783.980 1.990000e+00 2.65
322 10322 0 1.990 1.990000e+00 11
323 10323 0 1.990 1.990000e+00 3.6
324 10324 1 320.490 3.204900e+02 5.7
325 10325 0 1.990 1.990000e+00 1.03
326 10326 0 12.000 1.200000e+01 6.33
327 10327 0 3.010 7.838000e+01 2.5
328 10328 0 204.690 2.046900e+02 2.2
329 10329 1 336.950 1.200000e+01 15.7
330 10330 0 900.600 9.006000e+02 3.5
331 10331 0 1.990 1.990000e+00 1.1
332 10332 0 12.000 1.200000e+01 1.3
333 10333 0 159.710 1.597100e+02 3
334 10334 0 15.000 1.500000e+01 3
335 10335 0 296.310 2.963100e+02 2.17
336 10336 0 8012.990 4.176000e+03 3.3
337 10337 1 1.990 1.830600e+02 14.7
338 10338 1 1.990 1.990000e+00 2.8
339 10339 1 1.990 1.990000e+00 1.06
340 10340 0 1.990 2.000000e+01 9.1
341 10341 1 161.770 1.617700e+02 5.9
342 10342 1 61.980 6.198000e+01 8.9
343 10343 1 403.850 4.038500e+02 3.6
344 10344 0 136.710 1.990000e+00 2.31
345 10345 0 34.650 3.465000e+01 3.6
346 10346 0 48.860 4.886000e+01 2.8
347 10347 1 1.990 1.990000e+00 20.4
348 10348 0 371.740 3.717400e+02 1.5
349 10349 0 1.990 1.990000e+00 4.6
350 10350 0 1.990 1.990000e+00 5
351 10351 0 213.490 1.990000e+00 3.9
352 10352 1 970.750 9.707500e+02 9.8
353 10353 1 109.060 1.090600e+02 3.65
354 10354 0 1082.820 1.082820e+03 3.5
355 10355 0 739.130 7.391300e+02 10.7
356 10356 0 288.720 2.887200e+02 4.8
357 10357 1 4.680 1.990000e+00 4.5
358 10358 0 18.490 1.849000e+01 2.6
359 10359 0 169.330 1.693300e+02 3.9
360 10360 0 418.900 4.189000e+02 3.09
361 10361 0 1.990 1.990000e+00 10.9
362 10362 0 1.990 1.990000e+00 5.2
363 10363 0 14.830 5.382000e+01 1.6
364 10364 0 1.990 1.990000e+00 1
365 10365 0 650.710 1.990000e+00 6.4
366 10366 0 1.990 1.990000e+00 2.8
367 10367 1 1.990 1.990000e+00 6.4
368 10368 0 10.450 1.045000e+01 2.19
369 10369 1 764.830 7.648300e+02 15
370 10370 0 116.310 1.163100e+02 2.5
371 10371 1 785.420 3.001300e+02 4.2
372 10372 0 96.220 5.390000e+00 5.1
373 10373 1 346.590 3.465900e+02 6
374 10374 0 347.980 1.990000e+00 5.8
375 10375 0 6.190 6.190000e+00 0.2
376 10376 1 187.790 1.877900e+02 9
377 10377 0 459.630 4.596300e+02 1.2
378 10378 0 2.000 3.010000e+00 9.1
379 10379 0 2.000 2.000000e+00 2.9
380 10380 0 278.320 2.785200e+02 3.1
381 10381 0 1.990 1.990000e+00 5.6
382 10382 0 1.990 1.990000e+00 7.9
383 10383 0 10.840 1.084000e+01 18.9
384 10384 0 1.990 1.990000e+00 11.4
385 10385 0 184.420 1.844200e+02 3.8
386 10386 0 4.200 4.200000e+00 4.5
387 10387 0 1.990 1.990000e+00 2
388 10388 1 305.760 1.990000e+00 4.5
389 10389 0 278.320 2.783200e+02 3.1
390 10390 0 2.000 2.000000e+00 2.9
391 10391 1 2.000 3.010000e+00 9.1
392 10392 0 459.630 4.596300e+02 1.2
393 10393 1 187.790 1.877900e+02 9
394 10394 0 6.190 6.190000e+00 0.2
395 10395 0 347.980 1.990000e+00 5.8
396 10396 1 346.590 3.465900e+02 6
397 10397 0 96.220 5.390000e+00 5.1
398 10398 0 116.310 1.163100e+02 2.5
399 10399 1 232.640 2.326400e+02 1.6
400 10400 0 167.410 1.674100e+02 1.9
401 10401 0 1.990 1.990000e+00 1.14
402 10402 0 2.000 2.000000e+00 5.7
403 10403 1 230.530 2.305300e+02 28.6
404 10404 1 2.000 2.000000e+00 5.5
405 10405 0 10.400 1.040000e+01 3.8
406 10406 0 465.010 4.650100e+02 4.2
407 10407 0 1.990 1.990000e+00 0.5
408 10408 0 2.000 2.000000e+00 0.9
409 10409 0 1.990 1.990000e+00 4.8
410 10410 0 41.750 4.175000e+01 1.3
411 10411 0 363.080 8.540000e+00 6.4
412 10412 0 382.360 3.823600e+02 8.1
413 10413 1 1.990 1.990000e+00 10.8
414 10414 0 1.990 1.990000e+00 5.2
415 10415 1 2229.810 1.098000e+01 7.2
416 10416 1 252.720 2.527200e+02 15
417 10417 0 67.200 6.720000e+01 3.4
418 10418 0 1.990 1.990000e+00 2.28
419 10419 1 1.990 1.990000e+00 2.6
420 10420 0 1.990 1.990000e+00 4.8
421 10421 1 6.921 6.921000e+00 11.9
422 10422 0 1.990 1.990000e+00 6.9
423 10423 1 482.210 4.822100e+02 0.1
424 10424 0 40.220 1.990000e+00 12.8
425 10425 0 50.110 9.900000e-01 2.7
426 10426 1 1.990 1.990000e+00 6
427 10427 0 103.500 1.035000e+02 0.84
428 10428 1 102.870 1.028700e+02 2.9
429 10429 1 90.670 9.067000e+01 4.6
430 10430 1 375.180 3.751800e+02 20
431 10431 1 728.010 7.280100e+02 8.9
432 10432 0 433.970 4.339700e+02 1.2
433 10433 0 1.990 1.990000e+00 2.8
434 10434 0 1.990 1.990000e+00 10.3
435 10435 1 1.990 1.990000e+00 9.9
436 10436 0 1399.000 1.990000e+00 2.5
437 10437 0 213.140 2.131400e+02 3.6
438 10438 1 4.200 4.200000e+00 8
439 10439 1 86.420 2.381400e+02 1.01
440 10440 1 70.530 1.990000e+00 10.2
441 10441 0 1.990 1.812300e+02 11
442 10442 0 3.350 1.990000e+00 0.6
443 10443 0 407.250 4.918500e+02 0.5
444 10444 1 12.370 1.944000e+01 0.37
445 10445 1 1.990 1.990000e+00 16.9
446 10446 1 144.630 1.990000e+00 26.4
447 10447 0 30004.000 4.750400e+02 5.96
448 10448 1 30007.000 1.990000e+00 6.2
449 10449 0 1.900 1.990000e+00 9.1
450 10450 1 294.060 1.990000e+00 0.8
451 10451 1 53.510 1.312000e+01 0.91
452 10452 1 595.800 1.920000e+00 0.98
453 10453 1 482.870 1.990000e+00 0.89
454 10454 1 950.410 1.990000e+00 0.87
455 10455 1 355.510 1.990000e+00 9.7
456 10456 1 272.780 1.990000e+00 7.7
457 10457 1 148.520 1.990000e+00 1.1
458 10458 1 1.920 1.990000e+00 17.6
459 10459 1 515.330 1.990000e+00 6.6
460 10460 0 8.000 1.990000e+00 5
461 10461 0 152.670 1.990000e+00 7
462 10462 0 480.200 1.900000e+00 9
463 10463 1 381.990 1.500000e+01 0.7
464 10464 0 152.900 2.683700e+02 10
465 10465 1 598.080 1.990000e+00 0.85
466 10466 0 194.390 1.990000e+00 6
467 10467 0 1.990 1.990000e+00 6
468 10468 1 561.720 1.990000e+00 0.78
469 10469 0 10.000 1.990000e+00 16
470 10470 1 10.000 1.990000e+00 0.74
471 10471 1 1.980 8.000000e+00 0.91
472 10472 1 381.900 1.990000e+00 0.99
473 10473 1 10.000 9.350000e+00 0.9
474 10474 0 1.970 1.990000e+00 4.13
475 10475 0 4983.210 1.272000e+02 4.02
476 10476 1 1.990 5.640000e+01 6.09
477 10477 0 1.970 1.990000e+00 0.71
478 10478 1 501.310 1.990000e+00 3.62
479 10479 0 21.450 1.689900e+02 0.89
480 10480 0 696.830 1.990000e+00 1.97
481 10481 0 22.000 1.990000e+00 1.4
482 10482 0 1.890 1.990000e+00 3.03
483 10483 0 1.990 1.990000e+00 3.86
484 10484 1 70.350 1.990000e+00 5.75
485 10485 0 1.990 1.990000e+00 6.26
486 10486 0 1238.400 2.375000e+02 0.72
487 10487 1 710.400 1.990000e+00 10.53
488 10488 0 1.970 1.990000e+00 10.32
489 10489 0 37.190 1.990000e+00 2.39
490 10490 1 1.990 1.990000e+00 2.7
491 10491 0 1.990 1.990000e+00 1.1
492 10492 0 658.290 1.990000e+00 4.9
493 10493 0 1.990 1.990000e+00 10.6
494 10494 1 492.020 1.990000e+00 4.3
495 10495 0 341.470 1.990000e+00 5.1
496 10496 1 275.600 1.990000e+00 16.8
497 10497 0 336.510 1.990000e+00 7.9
498 10498 0 5.650 1.990000e+00 3.1
499 10499 0 1.990 1.990000e+00 5.1
500 10500 0 296.570 1.990000e+00 11.2
501 10501 0 1.990 1.990000e+00 1.7
502 10502 0 1.990 1.990000e+00 5.2
503 10503 0 469.550 1.990000e+00 2.2
504 10504 0 147.900 1.990000e+00 1.3
505 10505 0 6.000 1.024000e+01 12
506 10506 0 1.990 1.990000e+00 3.3
507 10507 0 300.530 1.990000e+00 1.7
508 10508 0 316.490 1.990000e+00 0.9
509 10509 0 1.990 1.990000e+00 4.9
510 10510 1 856.110 1.990000e+00 16.6
511 10511 0 1.990 1.990000e+00 17.6
512 10512 0 42.610 1.990000e+00 4.6
513 10513 1 121.050 1.990000e+00 5.5
514 10514 0 1.990 1.990000e+00 2.2
515 10515 0 171.850 1.990000e+00 18.9
516 10516 0 1.990 1.990000e+00 10
517 10517 0 1.990 1.048700e+02 16
518 10518 0 320.070 1.990000e+00 5.4
519 10519 0 31.770 1.990000e+00 3.7
520 10520 0 1.990 1.990000e+00 5.5
521 10521 1 632.220 1.990000e+00 7.2
522 10522 0 212.750 1.990000e+00 2.5
523 10523 0 157.680 1.990000e+00 18.5
524 10524 1 2.140 1.990000e+00 7.7
525 10525 1 1.990 1.990000e+00 0.19
526 10526 0 1.990 1.990000e+00 0.6
527 10527 0 83.950 1.990000e+00 2.5
528 10528 0 18.750 1.990000e+00 3.7
529 10529 0 1.990 1.990000e+00 0.8
530 10530 0 1.990 1.990000e+00 2.3
531 10531 0 270.490 1.990000e+00 1
532 10532 0 1.990 1.990000e+00 0.7
533 10533 0 667.070 1.990000e+00 6.3
534 10534 1 572.860 5.810000e+00 19.6
535 10535 0 615.290 1.990000e+00 18.2
536 10536 0 1.990 1.990000e+00 7.6
537 10537 0 1.990 1.990000e+00 1.7
538 10538 0 80.130 1.990000e+00 5.6
539 10539 0 1.990 1.990000e+00 3.7
540 10540 0 292.920 1.990000e+00 5.2
541 10541 1 1.990 1.990000e+00 20

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Displaying the pcos2 data:

Table 2.pcos2 data
Sl..No Patient.File.No. PCOS..Y.N. Age..yrs. Weight..Kg. Height.Cm. BMI Blood.Group Pulse.rate.bpm. RR..breaths.min. Hb.g.dl. Cycle.R.I. Cycle.length.days. Marraige.Status..Yrs. Pregnant.Y.N. No..of.aborptions I…beta.HCG.mIU.mL. II….beta.HCG.mIU.mL. FSH.mIU.mL. LH.mIU.mL. FSH.LH Hip.inch. Waist.inch. Waist.Hip.Ratio TSH..mIU.L. AMH.ng.mL. PRL.ng.mL. Vit.D3..ng.mL. PRG.ng.mL. RBS.mg.dl. Weight.gain.Y.N. hair.growth.Y.N. Skin.darkening..Y.N. Hair.loss.Y.N. Pimples.Y.N. Fast.food..Y.N. Reg.Exercise.Y.N. BP._Systolic..mmHg. BP._Diastolic..mmHg. Follicle.No…L. Follicle.No…R. Avg..F.size..L…mm. Avg..F.size..R…mm. Endometrium..mm. X
1 1 0 28 44.6 152.000 19.3 15 78 22 10.48 2 5 7.0 0 0 1.990 1.99 7.950 3.680 #NAME? 36 30 #NAME? 0.680 2.07 45.16 17.100 0.570 92.0 0 0 0 0 0 1 0 110 80 3 3 18.0 18.00 8.50
2 2 0 36 65.0 161.500 #NAME? 15 74 20 11.70 2 5 11.0 1 0 60.800 1.99 6.730 1.090 #NAME? 38 32 #NAME? 3.160 1.53 20.09 61.300 0.970 92.0 0 0 0 0 0 0 0 120 70 3 5 15.0 14.00 3.70
3 3 1 33 68.8 165.000 #NAME? 11 72 18 11.80 2 5 10.0 1 0 494.080 494.08 5.540 0.880 #NAME? 40 36 #NAME? 2.540 6.63 10.52 49.700 0.360 84.0 0 0 0 1 1 1 0 120 80 13 15 18.0 20.00 10.00
4 4 0 37 65.0 148.000 #NAME? 13 72 20 12.00 2 5 4.0 0 0 1.990 1.99 8.060 2.360 #NAME? 42 36 #NAME? 16.410 1.22 36.90 33.400 0.360 76.0 0 0 0 0 0 0 0 120 70 2 2 15.0 14.00 7.50
5 5 0 25 52.0 161.000 #NAME? 11 72 18 10.00 2 5 1.0 1 0 801.450 801.45 3.980 0.900 #NAME? 37 30 #NAME? 3.570 2.26 30.09 43.800 0.380 84.0 0 0 0 1 0 0 0 120 80 3 4 16.0 14.00 7.00
6 6 0 36 74.1 165.000 #NAME? 15 78 28 11.20 2 5 8.0 1 0 237.970 1.99 3.240 1.070 #NAME? 44 38 #NAME? 1.600 6.74 16.18 52.400 0.300 76.0 1 0 0 1 0 0 0 110 70 9 6 16.0 20.00 8.00
7 7 0 34 64.0 156.000 #NAME? 11 72 18 10.90 2 5 2.0 0 0 1.990 1.99 2.850 0.310 #NAME? 39 33 #NAME? 1.510 3.05 26.41 42.700 0.460 93.0 0 0 0 0 0 0 0 120 80 6 6 15.0 16.00 6.80
8 8 0 33 58.5 159.000 #NAME? 13 72 20 11.00 2 5 13.0 1 2 100.510 100.51 4.860 3.070 #NAME? 44 38 #NAME? 12.180 1.54 3.97 38.000 0.260 91.0 1 0 0 0 0 0 0 120 80 7 6 15.0 18.00 7.10
9 9 0 32 40.0 158.000 #NAME? 11 72 18 11.80 2 5 8.0 0 1 1.990 1.99 3.760 3.020 #NAME? 39 35 #NAME? 1.510 1 19.00 21.800 0.300 116.0 0 0 0 0 0 0 0 120 80 5 7 17.0 17.00 4.20
10 10 0 36 52.0 150.000 #NAME? 15 80 20 10.00 4 2 4.0 0 0 1.990 1.99 2.800 1.510 #NAME? 40 38 #NAME? 6.650 1.61 11.74 27.700 0.250 125.0 0 0 0 0 0 0 0 110 80 1 1 14.0 17.00 2.50
11 11 0 20 71.0 163.000 #NAME? 15 80 20 10.00 2 5 4.0 1 2 158.510 158.51 4.890 2.020 #NAME? 39 35 #NAME? 1.560 4.47 13.47 18.100 0.360 108.0 0 0 0 0 0 0 0 110 80 7 15 17.0 20.00 6.00
12 12 0 26 49.0 160.000 #NAME? 13 72 20 9.50 2 5 3.0 0 1 1.990 1.99 4.090 1.470 #NAME? 39 33 #NAME? 3.980 1.67 21.10 29.180 0.250 100.0 0 0 0 0 0 0 0 120 80 4 2 18.0 19.00 7.80
13 13 1 25 74.0 152.000 #NAME? 17 72 18 11.70 4 2 7.0 1 0 1214.230 1214.23 2.000 1.510 #NAME? 45 40 #NAME? 6.510 7.94 22.43 31.400 0.300 125.0 1 1 1 1 1 1 1 120 80 15 8 20.0 21.00 8.00
14 14 0 38 50.0 152.000 #NAME? 13 74 20 12.10 2 5 15.0 0 0 1.990 1.99 4.840 0.710 #NAME? 39 33 #NAME? 1.480 2.38 15.62 21.200 0.400 91.0 0 0 0 0 0 0 0 110 70 3 3 18.0 17.00 5.60
15 15 0 34 57.3 162.000 #NAME? 13 74 22 11.70 2 5 9.0 0 0 1.990 1.99 7.450 3.710 #NAME? 38 30 #NAME? 1.510 0.88 19.60 24.900 0.260 116.0 0 0 0 1 1 1 0 120 80 4 1 19.0 21.00 5.50
16 16 0 38 80.5 154.000 #NAME? 13 78 22 11.40 2 5 20.0 0 0 1.990 1.99 9.510 2.510 #NAME? 44 41 #NAME? 1.180 0.69 92.65 9.700 0.300 116.0 1 0 0 0 0 0 0 120 80 1 3 14.0 20.00 3.90
17 17 0 29 43.0 148.000 #NAME? 13 80 20 11.10 2 5 2.0 1 0 8104.210 91.55 2.020 0.650 #NAME? 36 29 #NAME? 1.980 3.78 20.25 19.100 0.460 91.0 0 0 0 0 0 0 0 110 70 6 5 20.0 20.00 5.60
18 18 0 36 69.2 160.000 #NAME? 13 72 18 10.80 2 5 7.0 0 0 1.990 1.99 4.860 2.960 #NAME? 39 32 #NAME? 5.000 1.92 12.52 26.000 0.250 100.0 0 0 0 0 0 0 0 110 80 1 2 20.0 18.00 4.00
19 19 0 31 52.4 159.000 #NAME? 17 72 18 12.70 2 5 7.0 0 0 1.990 1.99 6.050 1.050 #NAME? 37 33 #NAME? 3.190 1 12.05 23.300 0.250 127.0 0 0 0 0 0 0 0 120 80 0 2 0.0 17.00 5.60
20 20 1 30 85.0 165.000 #NAME? 16 72 18 12.50 4 7 7.0 0 0 23.580 1.99 1.890 0.810 #NAME? 44 42 #NAME? 2.870 2.07 19.13 28.000 0.300 100.0 0 1 1 1 1 1 0 120 80 16 8 18.0 17.00 11.00
21 21 0 25 64.0 156.000 #NAME? 11 70 18 11.20 2 6 6.0 0 0 1.990 1.99 2.820 1.300 #NAME? 39 34 #NAME? 1.860 2.85 33.62 17.100 0.400 91.0 0 0 0 0 0 0 0 110 80 1 2 18.0 19.00 5.70
22 22 0 38 50.0 156.000 #NAME? 15 72 18 11.00 4 9 12.0 1 0 749.980 749.98 3.180 2.180 #NAME? 36 29 #NAME? 5.710 2.13 40.74 18.900 0.300 84.0 0 0 0 0 0 0 0 120 80 4 2 17.0 17.00 7.60
23 23 0 34 64.2 155.000 #NAME? 15 74 20 12.10 2 5 10.0 1 0 218.650 218.65 4.080 2.300 #NAME? 36 32 #NAME? 1.250 4.13 13.38 28.600 0.260 116.0 0 0 0 0 0 0 0 110 70 5 7 16.0 17.00 7.60
24 24 0 28 65.0 152.000 #NAME? 13 74 22 10.50 2 5 5.0 1 0 13.000 13 6.410 1.690 #NAME? 37 33 #NAME? 0.450 2.5 17.88 30.540 0.730 92.0 0 0 0 0 1 0 0 110 80 6 8 14.0 18.00 8.50
25 25 1 34 63.0 158.000 #NAME? 11 72 20 11.20 2 5 12.0 1 0 610.630 610.63 5.340 0.890 #NAME? 38 32 #NAME? 0.650 1.89 11.46 21.500 0.980 100.0 0 1 0 0 1 1 0 120 70 4 6 18.0 17.00 7.30
26 26 0 41 42.0 152.000 #NAME? 12 80 20 11.50 2 5 15.0 1 0 4490.180 4490.18 8.820 4.390 #NAME? 39 32 #NAME? 0.840 0.26 17.35 45.600 0.980 92.0 0 0 0 0 0 0 0 110 70 1 2 15.0 17.00 8.80
27 27 1 30 76.0 160.000 #NAME? 15 75 18 11.20 4 3 5.0 0 0 1.990 1.99 6.180 2.780 #NAME? 45 38 #NAME? 4.280 3.84 17.98 25.140 0.350 100.0 1 1 1 1 1 1 1 120 80 21 20 11.0 12.00 6.80
28 28 0 20 68.0 152.000 #NAME? 17 72 20 10.00 4 3 4.0 1 0 689.580 11.24 1.800 0.410 #NAME? 40 33 #NAME? 8.390 2.5 11.24 46.300 1.200 92.0 0 0 0 0 0 0 0 110 80 3 2 10.0 11.00 10.00
29 29 0 25 62.0 158.000 #NAME? 15 78 22 11.60 2 5 8.0 0 0 15.000 15 4.600 1.060 #NAME? 42 34 #NAME? 3.790 3.56 15.30 36.560 0.250 106.0 0 0 0 0 0 0 0 120 80 7 4 12.0 17.00 6.20
30 30 1 28 56.0 152.000 #NAME? 13 78 22 11.00 4 3 7.0 0 0 768.030 768.03 2.410 1.190 #NAME? 40 33 #NAME? 3.440 1.56 45.47 23.140 0.630 100.0 1 0 1 0 1 1 0 110 80 11 10 13.0 14.00 5.00
31 31 0 32 50.0 155.000 #NAME? 17 78 22 10.80 2 4 3.5 1 0 612.320 12 6.340 4.070 #NAME? 34 28 #NAME? 1.400 1.69 54.09 56.200 0.310 80.0 0 0 0 0 0 0 0 110 80 10 8 14.0 14.00 7.00
32 32 1 34 57.0 150.000 #NAME? 11 72 22 12.00 2 4 15.0 0 0 10.000 10 5.010 6.480 #NAME? 40 37 #NAME? 4.210 1.89 12.71 32.450 0.270 85.0 1 1 1 1 1 1 0 110 80 5 10 14.0 13.00 6.00
33 33 0 31 58.0 155.000 #NAME? 15 72 18 10.50 2 5 2.0 0 0 1.990 1.99 2.800 0.420 #NAME? 39 32 #NAME? 1.880 2.34 23.74 32.200 0.520 138.0 0 0 0 0 0 0 0 120 80 8 9 12.0 13.00 7.50
34 34 0 38 54.0 165.000 #NAME? 15 72 18 10.20 2 5 15.0 0 3 20.000 20 6.110 1.970 #NAME? 42 34 #NAME? 2.420 1.58 40.41 36.850 0.620 92.0 0 0 0 0 0 0 0 120 80 2 5 14.0 12.00 8.50
35 35 0 28 50.0 158.000 #NAME? 15 76 20 10.00 2 5 6.0 1 0 1277.490 30.66 2.170 0.570 #NAME? 32 27 #NAME? 1.660 2.36 55.20 36.870 0.600 85.0 0 0 0 0 0 0 0 120 70 11 7 14.0 19.00 8.00
36 36 0 32 71.0 165.000 #NAME? 11 72 22 12.00 2 5 10.0 1 0 1455.000 1455 2.650 0.520 #NAME? 42 36 #NAME? 2.030 3.64 19.78 25.220 0.450 80.0 0 0 0 0 0 0 0 110 80 1 1 11.0 12.00 7.00
37 37 0 37 73.0 158.000 #NAME? 15 74 18 10.50 4 7 12.0 0 0 18.000 1.99 3.220 0.480 #NAME? 45 38 #NAME? 3.570 2.78 28.88 36.580 0.270 92.0 0 0 0 0 0 0 0 110 80 6 2 11.0 13.00 10.00
38 38 0 26 72.0 157.000 #NAME? 11 70 18 11.00 2 5 1.0 0 0 12.000 1.99 6.040 2.320 #NAME? 42 34 #NAME? 65.000 0.88 30.59 20.250 0.880 90.0 0 0 0 0 0 0 0 120 80 1 2 6.5 8.50 10.00
39 39 0 36 53.0 158.000 #NAME? 13 78 22 11.00 2 5 17.0 0 0 10.000 1.99 3.990 1.990 #NAME? 39 32 #NAME? 2.376 2.36 19.64 45.550 0.650 100.0 0 0 0 0 0 0 0 110 80 7 5 8.0 7.00 15.00
40 40 0 20 74.0 171.000 #NAME? 13 74 16 11.10 4 0 1.0 0 0 1.990 1.99 3.520 9.170 #NAME? 42 36 #NAME? 2.010 0.33 12.49 38.630 0.320 80.0 0 0 0 0 0 0 0 110 70 6 12 13.0 12.00 0.00
41 41 0 32 40.0 156.000 #NAME? 15 78 22 10.50 2 5 6.0 0 0 10.000 1.99 7.870 4.890 #NAME? 34 30 #NAME? 5.000 2.35 31.32 56.200 0.250 107.0 0 0 0 0 0 0 0 110 80 5 8 14.0 14.00 7.30
42 42 0 29 78.0 165.000 #NAME? 13 72 16 12.80 2 5 2.0 1 0 497.410 497.41 5.600 2.130 #NAME? 42 36 #NAME? 0.930 3.88 20.46 14.360 85.000 92.0 0 0 0 0 0 0 0 110 80 8 10 11.0 12.00 6.50
43 43 0 28 33.0 157.000 #NAME? 13 78 18 11.50 4 9 3.5 0 0 10.000 1.99 3.340 0.250 #NAME? 32 26 #NAME? 3.400 3.55 6.59 42.300 0.250 84.0 0 0 0 0 0 0 0 100 70 0 1 0.0 12.00 6.50
44 44 0 24 68.0 165.000 #NAME? 15 72 16 11.00 2 5 3.5 1 0 161.147 167 2.790 1.970 #NAME? 39 33 #NAME? 1.480 4.33 0.40 25.366 0.250 100.0 0 0 0 0 0 0 0 110 70 6 8 8.0 8.00 7.12
45 45 1 29 59.0 158.000 #NAME? 17 78 22 12.00 4 7 7.0 1 0 1305.350 1.99 3.260 0.790 #NAME? 39 33 #NAME? 3.770 3.55 31.36 44.550 0.760 100.0 1 1 1 1 1 1 1 120 80 13 14 16.0 15.00 7.00
46 46 0 25 56.0 155.000 #NAME? 15 70 18 10.60 2 5 6.0 1 0 43.500 9.83 2.770 0.570 #NAME? 40 33 #NAME? 3.850 2.36 25.60 45.500 0.310 92.0 0 0 0 0 0 0 0 120 80 5 8 14.0 13.00 9.40
47 47 1 28 75.0 165.000 #NAME? 15 72 18 12.00 2 5 2.5 1 0 141.060 141.06 5.810 0.270 #NAME? 45 41 #NAME? 0.830 3.66 14.43 52.300 0.380 100.0 1 1 0 1 1 1 0 110 70 4 6 10.0 12.00 9.00
48 48 0 26 40.0 156.000 #NAME? 13 74 18 10.20 2 5 3.0 1 0 1500.000 528.5 7.340 2.780 #NAME? 39 33 #NAME? 4.450 4.33 24.58 54.620 0.250 80.0 0 0 0 0 0 0 0 110 70 5 7 15.0 18.00 16.00
49 49 0 34 51.0 155.000 #NAME? 11 78 24 10.80 2 5 11.0 0 0 10.000 1.99 2.460 0.510 #NAME? 39 34 #NAME? 2.160 1 7.97 35.660 0.250 75.0 0 0 0 0 0 1 0 110 80 6 8 10.0 15.00 9.00
50 50 1 27 51.0 154.000 #NAME? 15 72 18 12.80 4 3 4.0 0 0 10.000 1.99 2.260 2.400 #NAME? 38 34 #NAME? 2.770 4.5 15.22 45.200 0.520 100.0 1 1 1 1 1 1 1 110 70 5 8 14.0 13.00 10.40
51 51 1 23 68.0 172.000 #NAME? 15 72 16 10.80 4 10 4.0 0 0 77.600 1.99 4.420 5.480 #NAME? 42 34 #NAME? 3.260 3.2 27.65 23.114 0.390 70.0 1 1 1 1 1 0 0 110 70 22 18 12.0 13.00 11.00
52 52 0 23 54.0 158.000 #NAME? 16 72 18 10.80 2 5 2.0 1 0 177.570 177.57 5.320 1.800 #NAME? 38 32 #NAME? 5.170 2.1 24.50 14.000 0.250 70.0 0 0 0 0 0 0 0 100 70 7 10 15.0 13.00 10.00
53 53 1 31 50.0 157.000 #NAME? 15 72 20 10.80 2 4 1.5 1 0 160.770 10 7.080 4.090 #NAME? 39 32 #NAME? 2.240 4.5 21.38 64.200 0.570 107.0 1 0 1 0 1 1 1 120 80 6 11 12.0 11.00 6.00
54 54 0 32 62.0 151.000 #NAME? 15 70 18 10.50 2 5 13.0 1 0 180.330 12 2.490 0.720 #NAME? 45 39 #NAME? 0.750 6.55 18.51 45.360 0.250 92.0 0 0 0 0 0 0 0 120 80 4 6 13.0 14.00 6.80
55 55 0 32 58.0 152.000 #NAME? 13 70 18 10.00 2 5 16.0 0 0 1.990 1.99 9.430 6.080 #NAME? 40 36 #NAME? 2.820 1.2 33.78 56.250 0.450 100.0 0 0 0 0 0 0 0 120 80 5 9 10.0 11.00 6.30
56 56 0 37 68.0 169.000 #NAME? 15 72 18 10.00 2 5 7.0 1 0 274.700 15 3.510 0.980 #NAME? 44 37 #NAME? 5.200 2.33 34.42 25.600 0.250 92.0 0 0 0 0 0 0 0 110 80 6 7 9.5 11.00 8.90
57 57 0 38 56.0 160.000 #NAME? 11 72 20 10.80 2 5 15.0 1 0 1.990 1.99 8.490 2.420 #NAME? 40 34 #NAME? 4.450 3.22 36.45 45.300 0.420 130.0 0 0 0 0 0 0 0 120 80 6 5 11.0 13.00 8.60
58 58 0 36 58.0 152.000 #NAME? 14 72 20 10.00 2 5 13.0 1 1 249.870 12 3.880 0.610 #NAME? 42 35 #NAME? 1.980 2.333 13.47 31.220 0.490 82.0 0 0 0 0 0 0 0 110 80 3 2 12.0 13.00 9.30
59 59 0 26 75.0 158.000 #NAME? 13 72 18 10.50 2 5 8.0 0 0 1.990 1.99 6.870 3.340 #NAME? 45 40 #NAME? 2.330 2.31 9.35 25.650 0.270 92.0 0 0 0 0 0 0 0 110 80 3 3 16.0 17.00 14.00
60 60 0 26 67.0 137.000 #NAME? 15 74 20 11.00 4 9 12.0 0 0 1.990 1.99 5.900 2.730 #NAME? 44 39 #NAME? 1.700 2.36 15.19 45.350 0.660 92.0 0 0 0 0 0 0 0 120 70 5 7 14.0 15.00 11.00
61 61 0 29 63.0 162.000 #NAME? 13 78 22 11.20 2 5 4.0 1 0 626.920 65.03 6.070 3.870 #NAME? 42 36 #NAME? 6.710 4.2 47.72 36.210 0.590 92.0 0 0 0 0 0 0 0 110 80 6 7 13.0 14.00 7.20
62 62 1 32 76.0 161.000 #NAME? 15 78 22 11.20 2 5 12.0 0 1 8.000 1.99 1.460 0.150 #NAME? 45 39 #NAME? 0.440 3.21 13.16 25.450 0.320 107.0 1 1 1 1 1 1 1 120 80 9 12 14.0 13.00 7.80
63 63 0 24 60.0 170.000 #NAME? 15 70 20 10.00 2 5 3.5 0 0 1.990 1.99 5.750 7.470 #NAME? 41 36 #NAME? 3.570 2.14 14.73 36.200 0.810 100.0 0 0 0 0 0 0 0 120 80 3 5 12.0 13.50 9.60
64 64 0 29 55.0 147.000 #NAME? 15 80 20 10.20 4 2 6.0 1 0 757.170 20 6.760 9.280 #NAME? 39 34 #NAME? 1.200 2.3 20.79 50.200 0.560 92.0 0 0 0 0 0 0 0 120 70 4 7 11.0 11.00 8.60
65 65 0 27 56.0 150.000 #NAME? 11 72 18 10.50 4 8 6.0 0 0 1.990 1.99 6.350 7.460 #NAME? 40 35 #NAME? 3.720 4.6 37.92 49.300 3.660 92.0 0 0 0 0 0 0 0 110 80 11 10 14.5 14.00 12.50
66 66 0 32 71.0 172.000 #NAME? 12 70 18 11.00 2 5 8.0 0 1 10.000 1.99 4.760 1.500 #NAME? 46 39 #NAME? 2.450 2.3 22.45 51.900 0.450 100.0 0 0 0 0 0 0 0 100 70 2 4 12.0 14.00 9.40
67 67 0 41 56.0 160.000 #NAME? 13 78 20 11.50 2 5 4.0 0 3 31.330 1.99 9.660 2.350 #NAME? 38 34 #NAME? 3.270 5.8 54.32 36.550 0.280 115.0 0 0 0 0 0 0 0 120 80 1 2 10.0 12.00 9.70
68 68 0 35 62.0 152.000 #NAME? 16 78 22 8.50 2 5 12.0 1 1 218.590 1.99 5.410 8.450 #NAME? 39 33 #NAME? 9.030 5.2 6.27 25.360 0.620 120.0 0 0 0 0 0 0 0 110 80 14 12 8.0 5.00 5.50
69 69 1 35 55.0 153.000 #NAME? 17 80 20 10.30 2 5 18.0 1 2 218.590 1.99 1.570 0.770 #NAME? 38 34 #NAME? 4.180 2.14 23.45 56.250 0.440 100.0 1 1 1 1 1 0 0 120 70 7 9 5.0 8.50 6.20
70 70 1 34 65.0 166.000 #NAME? 15 80 20 11.00 2 5 9.0 1 0 173.660 173.66 2.180 0.820 #NAME? 41 36 #NAME? 2.850 4.63 28.90 15.440 0.250 80.0 1 0 1 0 1 0 1 120 70 11 8 9.5 7.00 8.90
71 71 0 33 54.0 150.000 #NAME? 13 72 18 10.20 2 5 9.0 0 0 4.420 1.99 4.850 0.720 #NAME? 40 33 #NAME? 2.000 1.01 15.64 42.360 0.520 92.0 0 0 0 0 0 0 0 100 70 5 2 11.5 4.70 6.50
72 72 0 29 61.0 151.000 #NAME? 13 74 20 10.00 2 5 9.0 0 0 15.000 1.99 6.490 0.990 #NAME? 42 35 #NAME? 1.660 2.58 41.63 38.440 1.220 92.0 0 0 0 0 0 0 0 120 70 5 6 12.0 6.00 6.00
73 73 0 36 65.0 154.000 #NAME? 15 70 18 10.00 2 5 3.0 1 0 161.490 161.49 4.950 5.350 #NAME? 43 38 #NAME? 3.170 5.8 32.07 56.450 0.790 92.0 0 0 0 0 0 0 0 110 80 3 1 7.5 4.50 5.40
74 74 0 26 70.0 163.000 #NAME? 15 72 18 10.50 2 5 3.0 0 2 1.990 1.99 7.310 6.680 #NAME? 45 40 #NAME? 2.950 0.35 22.88 14.360 0.270 92.0 1 0 0 0 0 0 0 110 80 3 4 10.5 6.00 9.80
75 75 0 35 59.0 152.000 #NAME? 13 72 20 11.10 4 12 10.0 1 2 603.810 15 5.500 4.260 #NAME? 42 36 #NAME? 3.950 5.23 31.96 37.650 0.250 93.0 0 0 0 0 0 0 0 120 80 2 3 9.5 6.20 8.70
76 76 0 36 72.0 154.000 #NAME? 13 72 18 10.20 2 4 10.0 1 0 486.390 3.98 6.720 2.830 #NAME? 44 37 #NAME? 2.240 3.68 19.80 10.700 0.350 100.0 0 0 0 0 0 0 0 110 80 0 2 0.0 5.80 8.50
77 77 1 32 43.0 152.000 #NAME? 13 80 20 10.50 4 11 6.0 1 0 756.610 756.61 4.960 2.060 #NAME? 40 34 #NAME? 4.360 2.14 25.48 38.250 0.350 92.0 0 1 1 1 0 1 0 120 70 4 4 10.5 10.50 12.80
78 78 0 34 52.0 150.000 #NAME? 11 72 18 12.00 2 5 0.0 0 0 1.990 1.99 5.300 2.150 #NAME? 41 36 #NAME? 4.600 2.55 16.09 35.980 0.150 80.0 0 0 0 0 0 0 0 110 70 7 4 15.0 12.00 18.00
79 79 0 37 48.0 150.000 #NAME? 11 75 20 10.20 2 5 2.0 0 1 1.990 1.99 15.450 4.364 #NAME? 38 34 #NAME? 1.200 4.91 17.05 29.470 0.520 98.0 0 0 0 0 0 0 0 110 70 0 2 0.0 9.00 8.60
80 80 0 38 108.0 168.000 #NAME? 14 80 20 11.00 2 5 12.0 0 3 10.000 1.99 5.010 2.600 #NAME? 48 41 #NAME? 1.870 1.03 27.33 36.850 0.540 100.0 0 0 0 0 0 0 0 110 70 1 2 8.0 9.00 10.00
81 81 0 36 57.6 151.000 #NAME? 11 72 18 11.80 4 2 10.0 0 0 1.990 1.99 6.240 3.240 #NAME? 39 34 #NAME? 2.020 6.56 4.25 27.800 0.300 100.0 0 0 0 0 0 0 0 120 70 8 6 13.0 15.00 8.50
82 82 0 34 60.0 162.000 #NAME? 13 72 16 12.50 2 5 7.0 1 0 255.020 255.02 3.390 0.410 #NAME? 40 36 #NAME? 1.490 3.91 21.14 29.800 0.310 133.0 0 0 0 0 0 0 0 120 80 8 6 16.0 15.00 8.30
83 83 0 39 52.0 161.000 #NAME? 11 78 20 10.80 2 5 9.0 0 0 1.990 1.99 4.840 7.580 #NAME? 39 35 #NAME? 3.630 5.42 8.18 21.300 0.360 91.0 0 0 0 0 0 0 0 110 80 2 2 15.0 17.00 0.00
84 84 0 35 43.7 146.000 #NAME? 11 72 20 10.40 2 5 8.0 0 0 1.990 1.99 6.360 1.940 #NAME? 40 34 #NAME? 0.600 1.65 20.26 38.200 0.260 108.0 0 0 0 0 0 0 0 120 80 2 6 14.0 18.00 6.00
85 85 0 34 60.0 156.000 #NAME? 14 74 20 12.50 2 5 7.0 0 0 1.990 1.99 6.540 2.190 #NAME? 39 32 #NAME? 4.240 2.06 22.13 37.100 0.260 133.0 0 0 0 0 0 0 0 120 70 2 2 13.0 15.00 10.00
86 86 0 27 61.6 153.000 #NAME? 15 70 18 11.20 2 5 7.0 1 0 273.700 273.7 3.590 1.070 #NAME? 41 36 #NAME? 1.470 1.81 20.37 28.500 0.300 91.0 0 0 0 0 0 0 0 120 80 4 4 16.0 13.00 12.00
87 87 0 31 64.0 156.000 #NAME? 15 74 18 12.10 4 9 5.0 0 0 1.990 1.99 2.340 0.390 #NAME? 42 36 #NAME? 2.700 3.81 22.52 29.800 0.300 84.0 0 0 0 0 0 0 0 120 80 8 10 15.0 13.00 10.00
88 88 0 40 56.0 156.000 #NAME? 11 80 20 10.30 2 5 8.0 1 0 162.050 1.99 1.000 0.020 #NAME? 38 34 #NAME? 1.520 2.26 28.08 55.900 0.750 125.0 0 0 0 0 0 0 0 120 70 11 9 17.0 18.00 4.60
89 89 1 22 69.5 168.000 #NAME? 13 74 20 12.50 4 2 3.0 0 0 1.990 1.99 5.710 1.440 #NAME? 44 39 #NAME? 1.310 3.65 44.89 27.700 0.250 100.0 0 0 0 0 0 0 0 110 80 10 13 15.0 16.00 10.00
90 90 0 36 65.0 151.000 #NAME? 15 72 18 10.20 2 5 11.0 0 0 1.990 1.99 4.240 1.200 #NAME? 41 36 #NAME? 2.800 8.98 19.30 11.300 0.560 134.0 0 0 0 0 0 0 0 120 70 11 9 15.0 16.00 8.00
91 91 0 44 74.4 158.000 #NAME? 17 78 22 11.10 2 5 22.0 0 2 1.990 1.99 4.210 3.650 #NAME? 48 42 #NAME? 3.990 1.7 7.72 34.700 0.300 109.0 0 0 0 0 0 0 0 110 80 4 2 15.0 7.00 6.00
92 92 0 35 45.0 150.000 #NAME? 15 74 20 11.80 2 5 6.0 0 0 1.990 1.99 3.250 1.560 #NAME? 42 36 #NAME? 1.750 3.18 15.79 34.200 0.310 116.0 0 0 0 0 0 0 0 120 80 7 5 20.0 18.00 5.70
93 93 0 28 83.5 163.000 #NAME? 15 78 22 12.50 4 11 8.0 0 0 1.990 1.99 4.360 2.690 #NAME? 38 33 #NAME? 2.630 2.75 13.78 29.410 0.350 116.0 0 0 0 0 0 0 0 110 80 2 2 14.0 15.00 8.30
94 94 0 32 52.0 156.000 #NAME? 11 72 20 10.80 2 5 3.0 0 0 1.990 1.99 5.100 8.960 #NAME? 38 32 #NAME? 4.300 0.86 20.01 29.800 0.370 97.0 0 0 0 0 0 0 0 120 80 2 1 14.0 15.00 9.00
95 95 0 27 62.5 154.000 #NAME? 14 70 20 12.40 2 5 5.0 0 0 1.990 1.99 5.320 2.790 #NAME? 41 34 #NAME? 6.810 2.29 27.82 22.500 0.360 116.0 0 0 0 0 0 0 0 120 80 3 5 20.0 19.00 6.80
96 96 1 22 50.0 155.000 #NAME? 15 80 20 12.10 2 5 1.0 1 0 1247.560 15 2.000 1.000 #NAME? 38 32 #NAME? 2.780 2.19 27.86 18.700 0.300 107.0 1 1 1 1 1 1 0 120 70 6 10 19.0 19.00 6.00
97 97 1 28 67.5 154.000 #NAME? 16 78 24 11.70 2 5 2.0 0 3 15.000 1.99 4.660 2.430 #NAME? 41 36 #NAME? 4.640 8.46 28.93 18.700 0.400 104.0 0 0 0 0 0 0 0 110 70 18 13 17.0 18.00 5.90
98 98 1 31 91.4 154.000 #NAME? 15 78 24 10.70 4 12 10.0 1 0 320.560 14.46 3.680 2.080 #NAME? 39 32 #NAME? 5.000 4.59 40.04 28.300 0.410 108.0 1 1 1 1 1 1 0 110 70 13 10 19.0 16.00 5.00
99 99 0 32 61.0 163.000 #NAME? 11 72 20 11.00 2 5 13.0 1 0 145.890 145.89 5.280 3.950 #NAME? 40 39 #NAME? 4.360 1.04 30.98 19.200 0.360 125.0 0 0 0 0 0 0 0 120 70 3 2 20.0 18.00 5.00
100 100 0 32 61.7 156.000 #NAME? 15 78 22 12.20 2 5 8.0 0 5 5.030 1.99 5.640 0.790 #NAME? 41 34 #NAME? 4.140 4.27 13.66 37.200 0.400 60.0 0 0 0 0 0 0 0 110 80 4 2 18.0 18.00 7.50
101 101 0 30 62.2 158.000 #NAME? 13 72 18 10.80 2 5 5.0 1 1 100.090 100.09 1.480 0.300 #NAME? 42 37 #NAME? 1.440 3.86 20.08 16.900 0.980 116.0 0 0 0 0 0 0 0 120 80 7 10 19.0 20.00 6.80
102 102 0 34 74.1 162.000 #NAME? 11 78 22 12.40 4 11 13.0 0 0 1.990 1.99 1.470 0.300 #NAME? 41 34 #NAME? 1.560 1.42 13.18 44.500 0.300 131.0 0 0 0 0 0 0 0 110 80 5 2 17.5 18.00 6.40
103 103 1 38 64.3 149.000 #NAME? 11 78 20 12.90 4 2 16.0 1 1 110.170 110.17 10.520 2.530 #NAME? 42 36 #NAME? 1.320 10.07 14.52 70.530 0.100 83.2 1 1 1 1 1 1 0 110 70 12 12 17.0 9.00 11.30
104 104 0 34 57.0 160.000 #NAME? 15 72 20 11.80 2 5 12.0 1 1 174.370 174.37 9.400 3.720 #NAME? 38 32 #NAME? 4.050 0.98 17.55 24.300 0.380 92.0 0 0 0 0 0 0 0 120 80 1 0 14.0 0.00 7.00
105 105 0 29 60.0 159.000 #NAME? 13 72 18 11.20 4 9 6.0 1 0 5202.590 75.51 7.110 7.450 #NAME? 39 32 #NAME? 5.000 4.07 10.12 23.600 0.360 91.0 0 0 0 0 0 0 0 120 80 11 12 18.0 20.00 7.00
106 106 1 25 68.6 180.000 #NAME? 11 74 20 10.00 2 5 4.0 1 0 600.180 1.99 4.960 4.750 #NAME? 44 38 #NAME? 2.000 3.2 30.74 20.700 0.430 115.0 1 0 1 0 1 0 1 110 70 12 8 18.0 17.50 9.00
107 107 0 28 60.0 160.000 #NAME? 15 72 20 10.80 4 7 3.0 0 0 1.990 1.99 2.900 0.220 #NAME? 38 32 #NAME? 1.780 3.9 6.54 30.500 0.250 92.0 1 1 1 1 1 0 0 120 80 6 4 19.0 16.00 4.40
108 108 1 27 54.0 156.000 #NAME? 13 72 18 11.80 4 8 2.0 0 0 120.960 1.99 2.620 0.330 #NAME? 44 37 #NAME? 1.580 10 26.29 34.100 0.250 100.0 1 1 0 1 1 1 0 110 80 11 10 16.0 14.00 7.00
109 109 1 24 53.0 149.000 #NAME? 15 72 22 13.20 4 9 3.0 1 0 397.080 3893.06 4.620 6.030 #NAME? 45 39 #NAME? 1.460 16.9 9.82 15.300 0.980 120.0 1 1 1 0 0 1 1 110 70 11 10 15.0 16.00 9.00
110 110 0 34 80.0 160.000 #NAME? 15 74 20 10.00 4 11 19.0 1 0 926.240 600.23 4.860 1.970 #NAME? 42 37 #NAME? 1.040 17 19.45 39.400 0.340 108.0 0 1 1 0 0 1 1 120 70 5 4 17.0 21.00 9.30
111 111 0 21 59.0 150.000 #NAME? 15 72 18 11.20 4 6 2.0 1 0 26290.260 3350.19 4.130 5.110 #NAME? 41 34 #NAME? 1.260 21.9 13.97 33.400 1.000 125.0 0 0 1 1 0 0 1 100 70 1 1 11.0 10.00 8.50
112 112 0 26 75.0 170.000 #NAME? 15 72 18 11.20 4 2 3.0 1 0 32460.970 97.63 6.210 2.180 #NAME? 40 34 #NAME? 2.440 1.6 21.48 25.300 0.280 100.0 0 1 1 1 0 1 1 120 80 5 4 17.0 19.00 10.00
113 113 0 39 71.2 170.000 #NAME? 15 72 20 10.10 2 5 12.0 0 0 14.470 14.4 4.610 3.370 #NAME? 41 36 #NAME? 4.330 3.3 13.92 29.000 0.660 100.0 1 0 0 1 0 0 0 120 80 5 3 19.0 20.00 7.00
114 114 1 32 63.0 164.000 #NAME? 17 72 18 11.00 4 9 9.0 0 0 332.510 2 5.020 7.520 #NAME? 46 40 #NAME? 1.460 21 46.05 32.900 0.680 92.0 1 0 1 1 1 1 1 110 80 5 6 18.0 21.00 9.00
115 115 1 29 74.0 162.000 #NAME? 15 72 18 11.30 2 5 6.0 0 0 70.170 2 4.520 1.920 #NAME? 45 38 #NAME? 1.680 12.7 11.03 22.700 0.700 100.0 1 1 1 1 0 0 1 110 80 14 5 15.0 14.00 8.70
116 116 0 29 67.0 158.000 #NAME? 11 72 18 11.20 2 5 2.0 0 0 177.570 177.58 5.360 0.420 #NAME? 38 32 #NAME? 0.690 1.8 9.09 15.300 0.630 108.0 1 0 1 0 0 0 1 110 70 5 3 15.5 15.00 8.30
117 117 0 35 63.0 155.000 #NAME? 15 80 22 10.70 2 5 14.0 0 0 3.100 12.17 9.810 0.340 #NAME? 36 30 #NAME? 2.280 3.6 29.97 24.300 0.930 100.0 0 1 0 0 1 1 1 110 70 4 4 15.0 14.00 9.80
118 118 1 28 68.0 155.000 #NAME? 13 74 20 11.20 4 9 7.0 0 0 59.390 2 5.710 4.310 #NAME? 45 37 #NAME? 2.410 15 21.86 18.300 0.350 100.0 1 0 1 1 1 1 1 110 80 21 20 15.0 17.00 11.00
119 119 0 33 83.0 162.000 #NAME? 13 78 22 11.00 2 5 10.0 0 0 100.680 25.3 4.740 1.490 #NAME? 38 31 #NAME? 0.570 5 14.72 32.400 0.260 100.0 0 0 1 1 0 1 0 110 80 7 9 18.0 20.00 8.00
120 120 0 29 55.0 152.000 #NAME? 11 72 18 11.20 2 5 3.0 0 0 566.240 100.2 3.620 4.250 #NAME? 39 32 #NAME? 1.670 3.3 12.56 17.500 0.480 100.0 1 0 0 0 0 0 0 110 80 2 4 18.0 20.00 11.70
121 121 0 30 63.0 159.000 #NAME? 15 76 20 10.50 4 9 5.0 0 0 1.990 479.66 7.780 14.690 #NAME? 40 33 #NAME? 2.900 3.3 24.15 56.300 0.440 100.0 0 0 0 0 0 1 1 110 70 3 2 16.0 14.00 8.60
122 122 0 33 48.0 148.000 #NAME? 16 70 18 10.00 2 5 12.0 0 0 357.090 1.9 7.600 0.480 #NAME? 38 30 #NAME? 1.670 3.9 27.45 33.400 6.390 100.0 0 1 0 0 0 0 0 110 80 1 1 15.0 18.00 6.20
123 123 1 22 79.0 155.000 #NAME? 11 72 18 11.30 2 5 4.0 0 0 138.680 586.06 4.140 4.060 #NAME? 45 37 #NAME? 2.900 17.9 21.92 22.920 0.420 92.0 1 0 1 0 1 1 1 110 80 6 10 16.0 15.00 13.40
124 124 1 23 40.0 150.000 #NAME? 15 74 18 10.80 2 5 2.0 0 0 1.990 1.99. 5.000 5.150 #NAME? 46 38 #NAME? 1.690 19.8 15.23 23.500 0.930 92.0 1 1 1 1 0 1 1 100 70 10 13 18.0 17.00 12.50
125 125 1 26 78.0 159.000 #NAME? 13 78 20 11.00 2 5 4.0 0 0 1.990 1.99 4.880 5.840 #NAME? 45 39 #NAME? 2.310 9.2 31.53 56.200 0.400 100.0 1 0 1 1 1 1 1 120 80 8 10 16.0 18.00 8.50
126 126 0 38 54.0 155.000 #NAME? 13 72 22 10.80 2 5 4.0 0 1 363.130 2.8 8.360 0.780 #NAME? 41 33 #NAME? 2.430 2.4 33.51 13.500 0.500 92.0 1 0 0 0 0 0 0 120 80 7 6 18.0 17.00 12.00
127 127 0 40 56.0 154.000 #NAME? 15 72 18 10.20 2 5 14.0 0 0 84.790 1.99 5.010 1.940 #NAME? 42 33 #NAME? 2.080 4.5 26.34 52.200 0.910 98.0 0 0 0 1 0 0 1 110 80 2 3 18.0 18.00 8.50
128 128 0 42 48.0 148.000 #NAME? 11 72 18 11.20 2 5 8.0 0 0 1.990 15.36 6.810 3.830 #NAME? 40 32 #NAME? 1.990 5.14 18.02 9.200 0.320 100.0 0 0 0 0 0 0 0 110 70 1 0 17.0 15.00 9.00
129 129 0 41 54.0 150.000 #NAME? 15 70 18 11.50 2 5 10.0 0 0 98.910 54.08 7.370 1.510 #NAME? 38 30 #NAME? 15.680 2.4 20.16 25.600 0.280 92.0 0 0 0 0 0 0 1 110 80 0 1 15.0 9.00 14.00
130 130 0 40 40.0 145.000 #NAME? 11 72 18 10.20 2 5 4.0 0 4 1.990 1.99 4.960 0.690 #NAME? 38 31 #NAME? 1.860 0.3 45.56 36.100 0.350 92.0 0 1 0 0 0 0 0 110 80 2 1 19.0 12.00 9.70
131 131 1 24 89.0 173.000 #NAME? 17 72 18 11.20 4 2 4.0 0 0 152.130 152.13 5.900 9.900 #NAME? 44 38 #NAME? 1.730 11.48 42.43 13.000 0.600 91.0 1 0 1 0 1 1 1 120 80 4 8 16.0 18.00 8.00
132 132 0 25 53.4 159.000 #NAME? 11 72 18 10.50 2 5 4.0 0 0 1.990 1.99 6.540 10.750 #NAME? 36 30 #NAME? 1.290 19.3 17.68 36.800 0.450 100.0 0 0 0 0 1 1 0 110 70 3 7 15.0 14.00 11.50
133 133 1 22 60.0 154.000 #NAME? 15 70 18 12.70 2 5 4.0 0 0 1.990 1.99 6.440 5.780 #NAME? 45 34 #NAME? 2.960 8.8 26.72 41.700 0.300 85.0 1 1 1 0 0 1 0 110 80 3 3 16.0 17.00 9.70
134 134 1 28 70.0 157.000 #NAME? 15 70 18 11.00 2 5 5.0 0 1 342.160 1.99 3.410 3.580 #NAME? 42 36 #NAME? 1.130 19 21.70 18.800 0.560 120.0 1 1 1 0 0 1 1 110 80 12 9 19.0 18.00 12.00
135 135 0 35 68.0 154.000 #NAME? 15 72 18 12.00 2 5 10.0 0 0 3.050 3.05 4.830 1.150 #NAME? 38 30 #NAME? 2.260 4.3 14.79 19.200 0.410 85.0 0 0 0 0 1 0 0 120 80 3 4 18.0 17.00 9.00
136 136 0 30 71.0 160.000 #NAME? 15 72 18 12.00 2 5 10.0 0 2 1.990 1.99 6.020 3.400 #NAME? 39 31 #NAME? 1.330 1.4 13.87 26.870 0.720 100.0 0 0 1 0 0 0 0 120 80 4 7 15.0 17.00 11.00
137 137 0 26 60.0 153.000 #NAME? 13 78 22 11.50 2 5 4.0 0 0 201.360 201.36 3.590 2.540 #NAME? 37 30 #NAME? 0.250 12.6 8.12 26.600 0.580 123.0 1 0 0 1 0 0 1 110 80 4 4 14.0 13.00 11.00
138 138 0 35 40.0 144.000 #NAME? 17 72 18 10.80 2 5 4.0 0 0 1.990 1.99 3.080 1.280 #NAME? 39 33 #NAME? 1.510 4.8 55.47 30.900 0.530 100.0 0 1 0 0 0 0 0 100 70 6 4 17.0 18.00 10.70
139 139 0 27 59.0 160.000 #NAME? 15 70 18 10.80 2 5 5.0 1 0 21977.290 16069.69 3.140 0.090 #NAME? 40 34 #NAME? 0.040 4.6 19.99 47.000 0.530 84.0 0 0 0 1 1 0 0 110 70 8 5 18.0 15.00 9.00
140 140 0 35 65.0 154.000 #NAME? 15 72 18 10.70 2 5 5.0 0 0 1.990 1.99 2.360 1.160 #NAME? 38 30 #NAME? 3.850 17.1 6.60 25.800 0.440 120.0 0 0 0 0 0 0 0 120 70 5 7 15.0 13.00 8.50
141 141 0 36 51.0 150.000 #NAME? 13 72 18 10.20 2 5 8.0 0 0 3.410 57.08 7.920 1.030 #NAME? 37 30 #NAME? 4.040 2.1 27.85 26.500 0.500 92.0 1 0 1 0 0 0 1 110 80 3 2 9.0 18.00 9.00
142 142 1 28 57.0 158.000 #NAME? 15 74 22 10.80 2 5 5.0 0 0 1.990 1.99 2.810 3.270 #NAME? 42 33 #NAME? 5.000 11.6 16.98 19.900 0.250 92.0 1 0 1 0 1 1 1 110 70 13 12 16.0 18.00 11.00
143 143 1 30 69.0 158.000 #NAME? 13 72 18 10.40 4 11 14.0 0 0 1.990 1.99 6.400 3.270 #NAME? 42 32 #NAME? 4.180 18.4 23.17 41.800 0.460 100.0 1 1 0 1 1 1 1 110 70 6 7 18.5 16.00 8.00
144 144 0 32 45.0 150.000 #NAME? 15 70 18 10.80 2 5 7.0 0 0 1.990 1.99 11.120 4.220 #NAME? 38 31 #NAME? 0.900 1.8 3.81 27.900 0.250 110.0 0 0 0 0 0 1 1 110 80 0 5 0.0 19.00 7.00
145 145 0 37 68.0 153.000 #NAME? 15 80 22 11.20 2 5 14.0 0 0 1.990 1.99 4.190 1.490 #NAME? 37 30 #NAME? 1.400 9.9 10.01 23.500 0.580 130.0 0 0 0 1 0 1 1 120 80 3 3 21.0 18.00 9.30
146 146 0 35 68.0 160.000 #NAME? 15 74 18 11.10 2 5 4.0 0 0 1.990 1.99 7.420 2.100 #NAME? 39 32 #NAME? 1.620 3.7 18.60 20.100 0.700 108.0 0 0 0 0 0 0 0 120 70 9 10 17.0 18.00 7.50
147 147 0 36 85.0 164.000 #NAME? 13 72 18 10.20 2 5 12.0 0 1 1.990 1.99 6.280 1.280 #NAME? 40 33 #NAME? 3.260 2.9 12.28 10.100 0.320 160.0 0 0 0 1 0 0 0 120 70 0 4 0.0 18.00 10.70
148 148 0 28 72.0 160.000 #NAME? 17 70 18 10.00 2 5 3.0 0 0 1.990 1.99 8.960 0.750 #NAME? 38 32 #NAME? 1.040 2 24.51 20.000 0.600 92.0 0 1 1 1 0 0 0 110 80 3 3 17.0 18.00 8.00
149 149 1 23 53.0 152.000 #NAME? 11 72 18 11.00 4 2 3.5 0 0 1.990 1.99 6.230 3.250 #NAME? 45 40 #NAME? 0.050 4 28.17 24.900 0.380 92.0 1 1 0 1 0 1 1 110 80 4 12 18.0 16.00 10.00
150 150 0 24 52.0 152.000 #NAME? 11 80 20 11.00 2 5 4.0 0 0 1.990 1.99 5.450 3.010 #NAME? 39 32 #NAME? 3.460 1.6 77.32 19.500 0.380 92.0 0 0 0 1 0 0 0 110 70 6 2 19.0 19.00 7.10
151 151 1 28 63.0 162.000 #NAME? 15 78 18 10.80 4 2 5.0 0 0 232.710 232.71 4.210 9.500 #NAME? 44 38 #NAME? 0.650 15.9 17.28 23.800 0.980 110.0 1 1 0 1 0 1 1 110 80 4 9 15.0 18.00 10.00
152 152 1 29 56.4 150.000 25.1 13 76 22 10.50 4 4 6.0 0 0 1.990 1.99 2.910 0.400 7.25 44 35 0.795 1.640 7.51 10.60 31.200 0.390 108.0 1 1 0 1 0 1 0 110 80 12 14 14.0 18.00 8.00
153 153 1 31 60.0 156.000 24.7 13 72 18 11.90 4 5 10.0 0 1 1.990 1.99 2.680 0.650 4.12 46 36 0.782 1.000 10.04 13.01 41.890 0.240 100.0 1 1 1 1 1 1 0 110 80 7 5 15.0 16.00 8.50
154 154 1 29 52.0 147.000 24.1 11 74 18 11.50 4 4 8.0 0 0 1.990 1.99 6.650 12.950 0.513 40 35 0.875 1.890 6.86 27.58 21.220 0.440 116.0 1 0 0 1 0 0 0 110 80 3 5 16.0 19.00 8.00
155 155 0 26 40.0 152.400 17.2 11 72 20 10.80 2 5 3.0 1 0 60.800 23.5 6.560 5.220 1.25 38 32 0.842 1.660 7.02 23.69 14.500 0.250 100.0 0 0 0 1 0 0 0 120 80 6 5 12.0 14.00 14.00
156 156 0 26 49.3 158.000 19.7 15 76 18 12.50 4 4 4.0 0 0 1.990 1.99 5.090 3.130 1.626 45 35 0.777 2.560 8.75 44.27 9.600 0.250 73.0 0 1 0 0 0 0 0 120 80 7 7 14.0 16.00 7.50
157 157 0 27 53.2 158.000 21.3 13 72 22 10.50 4 4 5.0 0 0 1.990 1.99 3.090 0.590 5.237 37 32 0.864 2.030 5.27 7.16 25.300 0.360 91.0 0 0 1 1 0 NA 1 120 70 5 7 11.0 13.00 11.00
158 158 0 34 64.0 158.000 #NAME? 15 73 20 11.80 2 6 12.0 1 1 289.360 180.3 6.980 1.830 #NAME? 42 34 #NAME? 2.960 1.4 24.89 56.900 0.400 110.0 1 0 1 1 1 1 0 120 80 3 5 12.0 12.00 10.00
159 159 1 25 60.0 151.000 #NAME? 15 74 18 11.70 2 4 3.0 0 0 1.990 1.99 6.960 5.110 #NAME? 40 34 #NAME? 3.060 9 25.04 33.600 0.250 100.0 1 1 0 1 1 1 0 110 70 9 7 13.0 16.00 9.00
160 160 0 32 64.0 158.000 #NAME? 15 73 18 11.20 2 5 10.0 1 2 366.800 102.3 4.330 2.480 #NAME? 38 32 #NAME? 1.510 3.56 8.21 31.500 0.210 91.0 0 0 1 1 0 0 0 110 80 5 7 11.0 12.00 9.50
161 161 0 36 50.0 143.000 #NAME? 13 72 20 10.50 2 6 15.0 1 1 96.200 1.99 1.630 0.250 #NAME? 36 32 #NAME? 0.810 3.41 18.06 18.500 0.450 116.0 0 0 0 1 1 0 0 120 80 3 5 11.0 13.00 6.00
162 162 0 38 70.0 170.000 #NAME? 13 75 20 12.50 2 5 18.0 0 3 1.990 1.99 2.500 2.530 #NAME? 40 34 #NAME? 1.520 0.45 13.56 24.000 0.320 92.0 1 1 1 0 0 0 0 12 80 1 1 11.0 12.00 5.00
163 163 1 23 65.0 158.000 #NAME? 15 73 20 11.20 2 4 3.0 0 0 1.990 1.99 4.420 5.500 #NAME? 42 35 #NAME? 1.790 2.53 26.64 54.600 0.250 100.0 1 0 1 0 0 1 1 120 70 10 11 14.0 16.00 9.40
164 164 0 32 60.8 156.000 #NAME? 13 72 18 11.50 2 5 10.0 0 0 1.990 1.99 5.010 2.620 #NAME? 38 32 #NAME? 1.930 0.29 17.70 9.400 0.250 92.0 1 0 0 1 0 1 1 120 80 3 2 10.0 11.00 8.50
165 165 0 35 56.0 145.000 #NAME? 17 74 18 11.50 4 5 15.0 1 1 481.300 481.3 7.720 3.350 #NAME? 40 33 #NAME? 5.510 2.6 38.93 21.500 1.260 108.0 1 0 1 0 1 0 1 120 80 2 3 13.0 14.00 4.60
166 166 0 32 46.0 150.000 #NAME? 11 73 26 11.90 2 6 10.0 0 0 1.990 1.99 3.990 0.510 #NAME? 36 30 #NAME? 2.040 2.83 28.71 60.200 0.150 84.0 0 0 0 1 1 1 0 110 60 5 5 14.0 13.00 6.50
167 167 0 29 62.0 154.000 #NAME? 11 74 18 13.40 2 5 6.0 1 1 563.800 563.8 6.110 4.190 #NAME? 38 32 #NAME? 3.270 1.89 26.00 15.000 0.380 91.0 1 0 0 0 1 1 0 120 80 4 6 12.0 12.00 4.00
168 168 1 29 60.0 150.000 #NAME? 15 73 20 10.70 2 6 10.0 0 0 2.590 1.99 10.206 2.010 #NAME? 38 30 #NAME? 10.890 2.01 8.45 13.600 0.410 106.0 1 1 0 1 1 1 0 120 80 11 12 14.0 15.00 10.00
169 169 1 30 72.3 158.000 #NAME? 15 74 20 10.80 4 4 8.0 0 0 1.990 1.99 1.000 0.980 #NAME? 40 34 #NAME? 16.990 2.83 15.77 22.800 0.250 95.0 1 1 1 1 1 1 0 120 80 9 12 14.0 16.00 9.00
170 170 1 33 59.6 163.000 #NAME? 11 72 20 11.50 4 5 10.0 0 1 4.360 4.32 3.840 1.220 #NAME? 40 32 #NAME? 2.480 5.67 6.06 54.700 0.250 100.0 1 1 0 1 1 1 1 120 80 18 20 16.0 15.00 10.70
171 171 0 28 54.0 157.000 #NAME? 15 73 19 11.00 2 6 6.0 1 0 833.500 230.5 4.730 3.230 #NAME? 36 34 #NAME? 1.220 1.68 23.54 33.550 0.250 115.0 0 0 0 1 1 0 0 120 80 12 14 13.0 15.00 9.60
172 172 1 29 60.0 156.000 #NAME? 13 74 18 11.20 4 4 8.0 0 0 1.990 1.99 5.430 6.820 #NAME? 42 36 #NAME? 3.550 3.65 16.41 26.600 0.120 91.0 1 1 0 1 1 1 0 120 80 13 12 15.0 16.00 8.50
173 173 0 31 50.0 149.000 #NAME? 13 75 18 10.40 2 6 12.0 1 1 2036.200 155.3 1.920 0.340 #NAME? 36 30 #NAME? 2.730 3.63 14.98 48.000 0.900 91.0 0 0 1 0 1 0 1 120 80 3 2 14.0 12.00 5.50
174 174 1 35 73.5 155.000 #NAME? 15 72 20 10.09 4 5 10.0 0 2 3.888 3.888 3.130 0.440 #NAME? 42 34 #NAME? 0.760 3.49 16.65 31.200 0.410 108.0 1 1 1 1 0 1 0 120 80 15 16 13.0 14.00 9.50
175 175 0 37 60.0 152.000 #NAME? 11 72 18 11.70 2 6 15.0 1 1 698.200 523.6 9.100 5.100 #NAME? 38 32 #NAME? 2.850 2.01 10.78 17.100 1.000 91.0 1 0 1 0 0 1 0 120 80 1 1 10.0 11.00 5.00
176 176 1 27 58.0 152.000 #NAME? 15 74 18 11.10 4 4 5.0 0 0 1.990 1.99 6.250 7.350 #NAME? 36 30 #NAME? 2.610 8 30.00 6.500 0.251 84.0 1 0 1 1 1 1 0 120 80 12 13 15.0 16.00 9.00
177 177 1 28 78.0 158.000 #NAME? 16 73 18 10.50 2 4 8.0 0 0 3.660 1.65 4.500 2.770 #NAME? 42 38 #NAME? 4.040 9 20.76 10.220 0.260 108.0 1 1 1 1 0 1 0 110 70 15 14 14.0 12.00 8.00
178 178 1 24 89.0 173.000 #NAME? 17 74 18 11.20 4 4 2.5 0 0 1.990 1.99 5.900 9.900 #NAME? 40 35 #NAME? 1.730 11.48 42.43 25.690 0.350 91.0 1 1 0 1 1 1 0 120 80 11 8 15.0 14.00 8.00
179 179 1 37 88.0 168.000 #NAME? 12 74 18 11.90 2 5 15.0 0 1 3.830 3.83 9.220 3.220 #NAME? 41 38 #NAME? 1.680 10.25 3.64 48.960 0.580 350.0 1 1 1 1 0 1 0 110 80 12 14 14.0 16.00 11.00
180 180 0 35 58.0 164.500 #NAME? 11 72 20 12.00 2 6 13.0 1 0 2045.300 569.1 6.270 5.280 #NAME? 36 32 #NAME? 6.950 2.36 23.98 27.200 0.250 100.0 0 0 0 1 1 0 0 140 70 3 5 10.0 11.00 5.00
181 181 1 30 70.0 150.000 #NAME? 16 74 18 11.70 4 4 8.0 0 0 1.990 1.99 1.980 0.032 #NAME? 38 32 #NAME? 1.980 32 14.13 74.500 0.047 100.0 1 1 0 1 0 1 0 120 80 8 6 12.0 11.00 4.50 .
182 182 0 30 59.0 167.700 #NAME? 13 73 20 13.80 2 5 8.0 0 0 1.300 1.99 2.050 1.020 #NAME? 38 34 #NAME? 1.840 3.38 35.68 39.810 0.380 116.0 0 1 1 0 0 1 1 130 80 15 16 13.0 14.00 9.30
183 183 0 42 50.0 152.000 #NAME? 11 73 18 10.80 2 5 18.0 0 3 1.990 1.99 8.010 4.390 #NAME? 36 34 #NAME? 1.890 1.35 4.27 14.500 0.320 91.0 0 1 0 0 1 0 0 110 80 1 1 11.0 12.00 5.50
184 184 0 36 64.0 150.000 #NAME? 13 72 18 12.50 2 5 13.0 1 0 323.000 236.5 4.320 1.600 #NAME? 38 35 #NAME? 2.260 2.38 14.18 63.300 0.290 100.0 1 1 0 1 1 0 0 120 80 3 4 13.0 12.00 9.00
185 185 0 26 60.0 164.500 #NAME? 14 73 18 12.10 2 6 4.0 1 0 896.600 896.6 5.380 4.350 #NAME? 38 32 #NAME? 2.560 5.78 12.56 15.200 0.170 108.0 0 0 0 1 1 1 0 120 80 4 5 10.0 13.00 9.00
186 186 0 27 54.0 158.400 #NAME? 15 72 18 11.50 2 5 5.0 0 0 1.990 1.99 2.630 1.120 #NAME? 36 30 #NAME? 1.000 4.66 23.95 41.000 0.250 100.0 0 0 1 1 0 1 1 110 80 5 7 13.0 14.00 6.50
187 187 1 32 66.0 155.000 #NAME? 17 74 18 12.50 4 5 10.0 0 1 2.580 2.58 2.000 1.240 #NAME? 39 35 #NAME? 1.680 1.99 60.20 18.300 0.250 93.0 1 1 0 1 0 1 0 120 80 7 8 13.0 12.00 5.90
188 188 0 43 66.3 157.000 #NAME? 14 75 18 11.70 2 5 19.0 1 2 569.300 569.3 3.760 3.180 #NAME? 38 34 #NAME? 1.720 1.28 2.51 46.900 0.630 91.0 1 0 0 1 0 0 0 110 80 2 3 11.0 12.00 9.70
189 189 0 28 76.0 154.000 #NAME? 16 72 20 12.50 2 6 8.0 1 0 108.660 108.66 6.530 3.620 #NAME? 40 34 #NAME? 2.920 3.99 6.72 21.000 0.570 116.0 0 1 0 1 0 1 0 120 80 1 2 13.0 13.00 6.30
190 190 1 23 58.0 159.000 #NAME? 15 74 18 10.80 4 4 2.5 0 0 1.990 1.99 3.260 2.190 #NAME? 37 34 #NAME? 0.550 5.69 25.35 8.600 0.190 133.0 1 1 0 1 1 1 1 110 80 7 9 16.0 15.00 13.00
191 191 0 34 54.0 153.000 #NAME? 13 73 18 12.80 4 4 12.0 0 0 1.990 1.99 6.890 4.750 #NAME? 36 30 #NAME? 1.610 7.81 14.76 54.010 0.110 98.0 0 1 0 0 1 0 1 120 80 6 6 11.0 12.00 6.00
192 192 1 29 63.0 153.000 #NAME? 17 74 18 11.00 4 3 8.0 0 0 3.990 3.99 3.630 1.020 #NAME? 39 35 #NAME? 2.660 6.41 29.08 6014.660 0.250 123.0 1 1 1 1 1 1 0 120 70 14 16 16.0 17.00 9.00
193 193 0 30 67.0 167.000 #NAME? 12 72 22 12.00 4 5 10.0 1 1 455.800 121.8 5.700 2.370 #NAME? 37 32 #NAME? 2.360 5.76 10.79 28.700 0.250 108.0 0 1 0 0 1 0 1 110 80 2 4 10.0 13.00 6.00
194 194 0 30 55.0 173.700 #NAME? 11 72 20 13.80 2 5 8.0 0 0 2.010 1.99 5.640 4.450 #NAME? 34 32 #NAME? 10.220 1.68 20.63 29.100 0.890 91.0 0 0 0 1 1 0 0 120 80 2 2 10.0 11.00 7.00
195 195 0 29 50.0 152.000 #NAME? 13 74 18 10.80 4 3 6.0 1 0 122.580 122.58 5.490 7.060 #NAME? 36 34 #NAME? 2.800 8.75 25.73 57.080 0.400 102.0 0 1 0 0 1 1 0 120 80 5 6 13.0 12.00 7.00
196 196 1 35 60.0 153.400 #NAME? 13 72 20 13.20 4 4 14.0 0 1 1.990 1.99 22.000 3.390 #NAME? 38 35 #NAME? 2.420 6.65 16.34 5418.600 0.310 100.0 1 0 1 1 1 1 0 120 80 8 10 15.0 13.00 5.50
197 197 1 35 56.0 156.000 #NAME? 14 73 18 12.10 2 4 15.0 0 0 2.100 1.99 2.557 1.170 #NAME? 38 34 #NAME? 3.030 4.15 26.18 6.077 0.470 138.0 1 1 0 1 1 1 1 120 80 12 11 14.0 14.00 5.60
198 198 0 28 70.0 158.000 #NAME? 15 72 18 12.00 2 5 6.0 0 0 1.990 1.99 5.300 2.720 #NAME? 39 35 #NAME? 2.490 1.86 10.87 61.580 0.790 90.0 1 1 0 1 0 1 0 110 80 5 7 12.0 14.00 9.00
199 199 0 32 49.0 154.000 #NAME? 11 72 20 11.20 2 5 12.0 1 1 689.100 355.28 7.090 3.810 #NAME? 35 32 #NAME? 1.361 2.04 23.24 47.360 0.250 92.0 0 0 0 1 1 1 0 110 70 6 4 10.0 14.00 6.00
200 200 1 25 58.0 160.000 #NAME? 15 74 18 11.90 4 3 3.0 0 0 1.990 1.99 3.800 3.570 #NAME? 38 36 #NAME? 3.900 7.25 30.78 55.600 0.250 90.0 1 1 1 1 0 1 0 110 80 12 12 14.0 17.00 10.50
201 201 0 30 47.0 158.000 #NAME? 15 73 18 11.20 2 6 10.0 0 0 3.440 1.99 8.990 3.100 #NAME? 36 32 #NAME? 1.710 1.04 28.67 22.000 0.240 91.0 0 0 0 1 1 1 1 120 8 2 1 11.0 14.00 6.00
202 202 0 28 56.0 158.000 #NAME? 13 72 20 10.80 2 5 8.0 1 0 122.300 122.3 5.200 4.970 #NAME? 37 35 #NAME? 2.740 1.91 14.00 9.010 0.140 100.0 1 0 0 1 0 1 0 120 80 2 4 12.0 12.00 9.80
203 203 0 28 80.0 163.000 #NAME? 15 74 20 11.80 2 6 6.0 1 0 596.200 596.2 3.080 4.080 #NAME? 40 36 #NAME? 1.710 2.3 10.30 62.000 0.900 116.0 1 1 1 1 1 1 0 120 80 3 2 12.0 14.00 9.90
204 204 1 23 49.0 145.000 #NAME? 14 73 18 13.20 4 4 4.0 0 0 1.990 1.99 4.070 2.590 #NAME? 36 34 #NAME? 2.040 5.61 10.22 15.360 0.790 108.0 1 1 0 1 0 1 0 110 80 14 16 17.0 15.00 10.60
205 205 0 30 55.0 162.000 #NAME? 12 72 18 11.20 2 6 10.0 0 0 4.600 2.8 6.060 3.790 #NAME? 37 35 #NAME? 2.260 3.02 30.71 48.630 0.410 91.0 0 0 0 1 1 1 1 120 70 5 6 12.0 15.00 8.50
206 206 1 28 62.0 160.000 #NAME? 15 73 18 12.00 4 5 7.0 0 0 2.100 1.99 3.440 0.770 #NAME? 39 35 #NAME? 0.750 5.25 23.12 43.680 0.320 140.0 1 1 1 0 1 1 1 110 80 15 18 15.0 19.00 8.00
207 207 1 24 83.0 161.000 #NAME? 15 75 18 12.60 4 4 2.5 0 0 2.500 1.99 3.300 0.790 #NAME? 42 38 #NAME? 2.590 2.38 30.33 63.100 0.320 110.0 1 1 1 1 1 1 0 120 80 16 15 12.0 14.00 5.50
208 208 0 32 60.0 145.000 #NAME? 15 73 18 11.20 4 5 12.0 1 2 2054.300 588.7 2.470 1.430 #NAME? 37 34 #NAME? 3.640 7 11.76 87.200 0.250 108.0 1 0 0 1 1 0 0 120 80 3 4 14.0 12.00 7.50
209 209 0 27 51.0 150.000 #NAME? 13 72 18 12.01 2 6 7.0 1 0 147.600 147.6 5.790 3.570 #NAME? 36 33 #NAME? 1.220 3.17 25.04 90.000 0.660 84.0 0 0 0 1 1 0 0 110 80 5 7 12.0 13.00 6.50
210 210 1 35 62.0 146.000 #NAME? 17 74 20 13.40 4 4 12.0 0 1 1.990 1.99 1.590 0.600 #NAME? 38 35 #NAME? 1.580 5.57 12.37 46.580 1.100 98.0 1 1 0 1 1 1 1 120 80 8 10 13.0 15.00 5.50
211 211 1 32 54.0 145.000 #NAME? 11 72 18 10.80 4 5 10.0 0 0 2.200 1.99 3.130 0.420 #NAME? 37 34 #NAME? 1.450 4.57 31.96 21.200 0.360 102.0 1 0 1 1 0 1 0 120 80 12 15 15.0 18.00 6.10
212 212 1 39 55.0 150.000 #NAME? 15 78 20 10.70 4 4 13.0 0 0 12.370 12.37 5.400 1.950 #NAME? 34 32 #NAME? 1.000 0.37 21.88 18.600 0.460 94.0 0 1 1 0 0 1 0 120 80 12 11 17.0 14.00 15.00
213 213 0 27 52.0 150.000 #NAME? 11 82 20 11.10 2 5 5.0 0 0 1.990 1.99 4.970 2.230 #NAME? 42 38 #NAME? 1.940 16.9 9.12 23.600 0.250 93.0 0 0 0 1 1 0 1 120 80 1 1 17.0 16.00 9.40
214 214 1 26 70.0 150.000 #NAME? 15 72 20 12.00 4 5 3.0 1 0 144.630 144.63 3.630 8.340 #NAME? 36 34 #NAME? 2.290 26.4 29.72 22.400 0.380 93.0 1 1 0 0 1 1 0 110 80 8 7 17.0 14.00 6.00
215 215 0 29 63.0 152.000 #NAME? 11 72 18 13.20 2 6 11.0 1 4 25000.000 475.04 1.990 1.610 #NAME? 38 34 #NAME? 1.770 5.96 21.95 22.700 0.460 95.0 1 0 0 1 1 1 0 110 80 6 7 16.0 14.00 5.00
216 216 0 41 71.0 160.000 #NAME? 15 74 20 11.00 4 3 16.0 0 1 1.990 1.99 4.740 3.600 #NAME? 40 36 #NAME? 1.090 9.1 22.62 31.600 0.250 85.0 1 0 0 1 1 1 1 120 80 9 7 14.0 14.00 10.00
217 217 1 36 66.0 162.000 #NAME? 15 72 20 11.00 2 5 13.0 1 2 515.530 515.53 1.630 0.170 #NAME? 36 34 #NAME? 1.860 6.6 25.00 0.000 0.250 93.0 1 1 1 1 1 1 1 120 80 14 6 15.0 13.00 9.70
218 218 1 34 72.0 158.000 #NAME? 13 72 18 11.00 4 4 13.0 0 1 1.990 99.69 4.760 2.700 #NAME? 38 35 #NAME? 2.300 22 12.46 22.700 0.250 93.0 1 1 0 1 1 1 0 110 80 9 12 15.0 13.00 10.00
219 219 0 26 47.8 158.000 #NAME? 13 72 20 10.20 2 6 7.0 1 0 70.420 70.42 6.280 4.040 #NAME? 34 32 #NAME? 2.110 1.9 11.83 23.800 0.250 120.0 0 0 0 1 1 0 0 120 80 6 6 17.0 16.00 10.20
220 220 0 27 76.8 155.000 #NAME? 13 78 20 11.10 4 4 1.0 0 0 342.910 342.91 7.260 5.330 #NAME? 37 35 #NAME? 6.070 4.3 23.54 21.800 0.250 93.0 1 0 0 1 1 1 0 120 80 3 2 14.0 15.00 14.00
221 221 0 39 61.0 152.000 #NAME? 11 72 18 11.00 2 4 20.0 0 3 1.990 1.99 8.910 1.490 #NAME? 36 34 #NAME? 2.310 0.37 14.22 25.500 0.250 93.0 0 0 0 1 0 0 1 120 80 1 1 16.0 12.00 10.00
222 222 1 31 31.0 158.000 #NAME? 15 80 20 12.00 4 3 3.0 0 0 1.990 1.99 3.990 6.630 #NAME? 32 28 #NAME? 1.300 17.6 13.91 19.570 0.250 133.0 0 1 1 1 0 1 0 120 70 7 6 14.0 16.00 10.00
223 223 1 27 65.0 158.000 #NAME? 17 78 20 11.10 2 5 5.0 1 0 148.520 148.52 4.960 1.750 #NAME? 35 32 #NAME? 2.200 1.1 12.11 25.700 0.350 93.0 1 1 1 0 1 1 0 120 70 14 10 16.0 15.00 8.00
224 224 0 30 62.0 169.000 #NAME? 11 18 20 12.00 2 5 5.0 0 0 1.990 1.99 7.040 4.090 #NAME? 38 34 #NAME? 1.540 7.8 19.60 31.800 0.250 95.0 0 0 0 1 0 1 1 120 70 4 3 14.0 18.00 8.70
225 225 0 36 62.0 152.000 #NAME? 11 70 18 10.00 2 6 11.0 0 0 1.990 1.99 4.590 0.580 #NAME? 38 36 #NAME? 2.670 2.9 45.46 18.700 0.350 106.0 1 0 1 1 0 1 0 110 80 6 6 17.0 16.00 7.70
226 226 0 32 52.0 150.000 #NAME? 13 70 18 9.40 2 5 12.0 0 0 272.780 272.78 4.330 2.110 #NAME? 35 33 #NAME? 2.100 7.7 40.47 41.000 0.250 93.0 0 0 0 1 0 0 1 110 80 6 7 16.0 15.00 12.00
227 227 0 35 61.0 158.000 #NAME? 13 72 18 10.50 2 6 13.0 1 1 355.510 355.51 3.710 2.670 #NAME? 36 34 #NAME? 2.970 9.7 20.35 15.000 0.250 93.0 0 1 0 0 1 1 1 120 80 12 13 14.0 17.00 13.00
228 228 1 29 75.0 163.000 #NAME? 15 72 18 10.70 2 6 3.0 0 0 1.990 1.99 3.270 1.560 #NAME? 37 35 #NAME? 2.380 0.2 34.73 10.100 0.430 85.0 1 1 1 1 1 1 0 110 80 1 3 11.0 12.00 6.00
229 229 0 31 61.0 147.000 #NAME? 13 74 20 12.30 2 4 10.0 0 2 1.990 1.99 7.800 1.540 #NAME? 40 38 #NAME? 2.060 2.5 10.68 10.400 0.250 83.0 1 0 0 1 1 1 1 120 80 2 4 16.0 12.00 10.80
230 230 0 28 74.3 154.000 #NAME? 13 72 18 10.70 2 5 6.0 0 3 1.990 1.99 3.700 4.600 #NAME? 38 36 #NAME? 1.700 12 46.25 10.800 0.770 85.0 1 0 1 1 0 1 0 110 80 6 5 14.0 13.00 8.50
231 231 1 40 71.0 152.000 #NAME? 15 72 18 13.20 2 5 7.0 0 0 1.990 1.99 2.780 0.980 #NAME? 38 35 #NAME? 3.780 1.4 29.03 20.800 0.360 160.0 1 1 0 1 0 1 0 120 80 4 2 17.0 10.00 12.00
232 232 0 22 78.0 154.000 #NAME? 15 72 18 12.40 2 5 9.0 0 1 150.910 150.91 5.210 2.190 #NAME? 40 36 #NAME? 1.240 16.7 10.02 20.800 0.270 77.0 1 0 0 0 1 0 0 120 80 1 1 13.0 10.00 8.80
233 233 1 35 55.0 162.000 #NAME? 13 72 18 12.40 4 4 7.0 0 0 451.960 1.99 2.630 1.050 #NAME? 35 32 #NAME? 2.080 13.6 28.85 32.200 0.340 86.0 0 1 1 0 1 1 0 120 80 16 14 15.0 18.00 11.50
234 234 0 31 50.0 152.000 #NAME? 15 72 18 10.00 2 6 4.5 0 1 392.730 1.99 5.370 8.150 #NAME? 34 30 #NAME? 25.910 16.8 14.41 23.910 0.250 92.0 0 0 0 0 1 1 1 120 80 7 5 17.0 15.00 9.00
235 235 0 27 45.0 152.000 #NAME? 13 72 18 11.50 2 6 4.0 0 0 391.460 391.46 1.850 0.100 #NAME? 32 30 #NAME? 3.790 3.5 29.32 22.300 0.250 73.0 0 1 1 0 1 0 1 120 80 1 3 14.0 16.00 8.50
236 236 0 28 58.1 153.000 #NAME? 13 72 18 12.40 2 5 4.0 0 0 418.820 1.99 7.500 1.480 #NAME? 36 34 #NAME? 0.730 1.3 27.73 31.100 0.250 86.0 0 1 0 0 0 1 1 110 70 0 5 17.0 18.00 4.80
237 237 0 33 54.0 146.000 #NAME? 15 72 18 12.80 2 3 8.0 0 1 1.990 1.99 3.210 0.570 #NAME? 38 35 #NAME? 10.960 3.14 44.96 28.700 0.250 80.0 0 0 0 0 1 1 1 120 80 5 5 18.0 20.00 7.00
238 238 1 25 42.0 154.000 #NAME? 14 72 18 12.50 4 3 4.0 0 0 1.990 1.99 6.580 3.090 #NAME? 33 30 #NAME? 0.850 1.25 12.81 30.200 0.670 100.0 1 0 0 1 1 0 0 120 80 6 5 15.0 17.00 9.00
239 239 0 32 66.0 164.000 #NAME? 15 72 18 11.10 4 4 13.0 0 0 464.120 464.12 3.840 1.530 #NAME? 34 31 #NAME? 0.950 7.7 34.94 16.700 0.250 98.0 0 0 1 0 1 1 1 120 80 5 5 17.0 19.00 9.00
240 240 0 20 56.0 159.000 #NAME? 13 72 18 12.40 2 5 1.5 1 0 389.260 41.77 5.900 3.130 #NAME? 35 33 #NAME? 0.610 7.3 10.60 22.000 0.250 86.0 0 0 0 0 1 0 1 120 80 10 5 15.0 18.00 8.00
241 241 1 24 53.6 166.000 #NAME? 15 72 18 10.70 2 5 3.0 1 0 1390.580 1390.58 3.900 0.990 #NAME? 34 32 #NAME? 5.000 7.2 40.28 30.700 0.260 86.0 0 1 0 1 1 0 0 110 80 12 9 15.0 18.00 8.00
242 242 0 23 60.0 157.000 #NAME? 17 72 18 11.50 2 6 2.0 0 0 1.990 1.99 6.090 2.150 #NAME? 38 35 #NAME? 1.370 3.29 29.11 26.000 0.250 93.0 0 0 0 1 1 0 0 110 80 6 4 16.0 19.00 14.00
243 243 0 37 65.0 159.000 #NAME? 13 72 20 12.60 2 5 10.0 0 0 1.990 1.99 1.320 0.990 #NAME? 36 33 #NAME? 2.000 2.69 15.20 35.800 0.362 113.0 1 1 0 1 0 1 0 120 80 6 5 15.0 18.00 9.00
244 244 1 29 63.0 152.000 #NAME? 15 72 18 11.50 2 6 2.0 0 0 213.830 213.83 3.180 1.000 #NAME? 40 37 #NAME? 2.470 2.1 29.62 31.600 0.250 92.0 1 1 1 1 0 1 0 120 80 4 7 10.0 15.00 9.20
245 245 1 39 104.0 164.400 #NAME? 15 72 18 12.00 2 6 11.0 0 1 1.990 1.99 6.400 2.260 #NAME? 46 44 #NAME? 5.000 4.1 13.39 32.600 0.250 106.0 1 1 1 1 1 1 0 120 80 9 7 16.0 13.00 9.60
246 246 1 23 57.0 153.000 #NAME? 15 73 18 12.00 2 4 3.0 1 0 353.600 45.9 3.180 1.290 #NAME? 38 35 #NAME? 0.900 6.2 23.09 30.100 0.250 106.0 0 0 1 1 1 1 1 120 80 8 10 17.0 18.00 9.00
247 247 0 26 54.0 163.000 #NAME? 15 74 20 10.00 2 5 2.0 0 0 30.600 18.36 4.100 2.040 #NAME? 36 34 #NAME? 0.300 14.6 13.27 12.200 0.250 93.0 0 0 0 1 0 1 1 120 70 1 2 14.0 16.00 8.00
248 248 1 24 79.0 165.000 #NAME? 16 72 18 10.20 2 5 4.0 0 0 154.480 154.48 3.070 1.870 #NAME? 42 38 #NAME? 4.070 4.71 31.40 24.000 0.250 86.0 1 1 1 0 1 1 0 110 80 10 15 18.0 19.00 10.30
249 249 1 33 65.0 153.000 #NAME? 17 72 18 12.00 4 5 1.0 0 0 1.990 1.99 5.420 7.850 #NAME? 38 36 #NAME? 1.310 1.25 111.74 12.800 0.250 146.0 1 1 1 0 1 1 0 110 70 7 10 16.0 19.00 12.00
250 250 0 29 52.0 157.000 #NAME? 15 72 18 14.20 2 4 1.5 0 1 1.990 1.99 4.700 1.600 #NAME? 35 33 #NAME? 0.290 1 23.79 39.700 0.250 94.0 0 0 0 1 0 0 0 120 70 4 6 18.0 17.00 11.00
251 251 1 45 85.0 153.000 #NAME? 15 72 18 12.00 4 4 18.0 0 0 50.430 1.99 65.400 0.200 #NAME? 44 40 #NAME? 2.960 11.1 21.70 18.700 0.250 86.0 1 1 1 1 1 1 0 120 80 10 15 15.0 17.00 8.30
252 252 0 30 63.8 148.000 #NAME? 11 72 18 11.50 4 3 7.0 0 0 1.990 1.99 7.380 3.460 #NAME? 40 37 #NAME? 2.930 1.9 15.28 21.200 0.250 80.0 0 0 0 0 0 1 1 120 70 0 1 17.0 15.00 13.00
253 253 1 47 62.7 154.000 #NAME? 15 72 18 10.00 4 4 30.0 1 2 25000.000 25000 1.880 0.250 #NAME? 38 36 #NAME? 2.470 6.2 31.47 12.100 0.250 92.0 1 1 1 1 0 0 0 110 80 14 11 16.0 19.00 9.30
254 254 0 36 46.0 150.000 #NAME? 15 72 18 13.30 2 5 18.0 0 0 1.990 1.99 1.580 0.260 #NAME? 34 32 #NAME? 4.860 1.9 26.08 26.300 0.250 105.0 0 0 0 0 1 0 0 110 80 1 3 18.0 14.00 6.00
255 255 0 28 52.0 156.000 #NAME? 13 72 16 11.20 2 5 11.0 0 1 41.250 1.99 8.940 3.540 #NAME? 34 32 #NAME? 1.760 1.5 19.23 29.500 0.250 100.0 0 0 0 0 1 0 0 120 70 2 3 15.0 18.00 8.00
256 256 0 36 32.0 154.000 #NAME? 15 72 20 12.50 2 6 15.0 0 0 1.990 638.52 1.320 0.950 #NAME? 32 30 #NAME? 0.920 2.9 5.27 31.500 0.400 80.0 0 0 0 1 1 0 0 120 80 5 7 16.0 17.00 8.20
257 257 0 33 35.0 150.000 #NAME? 13 74 20 12.00 2 5 10.0 0 1 413.620 1.99 3.880 0.470 #NAME? 30 28 #NAME? 1.420 2.25 29.89 27.600 0.250 107.0 0 0 0 1 1 0 0 120 70 4 1 18.5 19.00 8.50
258 258 0 27 48.0 155.448 #NAME? 15 72 18 12.50 2 6 6.5 0 0 1.990 1.99 1.760 0.100 #NAME? 33 30 #NAME? 1.170 6.8 22.51 13.700 0.400 72.0 0 0 0 1 0 1 0 110 80 2 5 17.0 18.00 9.00
259 259 0 43 33.0 152.000 #NAME? 15 72 18 11.10 2 6 6.0 0 0 221.280 1.99 12.280 2.520 #NAME? 30 28 #NAME? 1.250 0.8 10.99 22.400 0.250 100.0 0 0 0 1 1 1 1 120 80 9 8 18.0 19.00 10.00
260 260 1 31 50.0 158.496 #NAME? 11 74 20 12.50 2 5 4.0 0 0 227.360 1.99 2.120 0.370 #NAME? 34 32 #NAME? 5.010 7.21 27.01 32.100 0.250 100.0 0 1 1 1 0 1 0 120 80 10 15 17.0 18.00 8.00
261 261 0 38 34.0 153.000 #NAME? 13 72 18 14.80 2 5 8.0 0 1 1.990 1.99 2.950 0.490 #NAME? 32 30 #NAME? 2.610 4.2 30.24 24.600 0.390 100.0 0 0 0 1 0 1 0 130 80 1 3 15.0 17.00 8.40
262 262 0 32 35.0 150.000 #NAME? 11 70 18 11.50 2 4 4.0 0 0 643.410 1.99 1.700 0.460 #NAME? 32 30 #NAME? 2.840 6.2 19.27 20.900 0.250 72.0 0 0 0 0 0 1 0 110 80 1 0 19.0 20.00 10.00
263 263 0 38 53.5 158.496 #NAME? 13 72 18 12.50 2 5 14.0 0 0 1.990 1.99 1.800 0.490 #NAME? 35 33 #NAME? 1.560 0.9 45.78 23.500 0.250 87.0 0 0 0 0 0 0 0 110 70 4 1 17.0 13.00 6.50
264 264 0 37 45.0 156.000 #NAME? 17 74 20 12.10 2 5 10.0 0 0 3.250 4.76 10.390 4.110 #NAME? 35 32 #NAME? 1.710 16.8 33.58 43.900 0.250 100.0 0 0 0 1 1 0 0 120 80 5 5 19.0 18.00 8.70
265 265 0 34 35.0 155.000 #NAME? 13 74 20 12.40 2 4 8.0 0 0 165.610 1.99 4.940 2.300 #NAME? 32 29 #NAME? 3.360 16.9 23.62 16.400 0.260 88.0 0 0 0 1 1 0 0 110 70 10 7 18.0 18.00 9.00
266 266 0 40 32.0 152.000 #NAME? 15 72 20 10.00 2 3 2.0 0 1 1.990 1.99 5.010 1.600 #NAME? 30 28 #NAME? 5.000 8.5 23.30 27.700 0.270 88.0 0 0 0 0 0 1 1 120 80 6 5 18.0 17.00 8.00
267 267 0 25 64.4 157.000 #NAME? 11 72 18 11.50 2 5 4.0 0 0 92.920 1.99 6.930 2.860 #NAME? 34 32 #NAME? 4.000 3.9 21.92 30.800 0.400 84.0 0 0 0 1 1 0 0 110 80 3 2 19.0 18.00 5.60
268 268 1 28 58.9 159.000 #NAME? 11 72 20 12.40 2 6 1.0 0 0 117.140 18.13 7.350 3.610 #NAME? 36 34 #NAME? 1.710 66 15.37 30.200 0.440 70.0 0 1 1 1 0 1 1 120 80 7 10 19.0 19.00 10.00
269 269 0 25 53.5 152.000 #NAME? 11 72 18 12.40 4 3 3.0 0 0 100.580 1.99 5.460 0.910 #NAME? 32 30 #NAME? 3.250 26.8 33.07 21.400 0.250 70.0 0 0 0 1 0 0 0 110 80 6 5 17.0 19.00 7.00
270 270 1 26 59.0 154.000 #NAME? 17 70 18 10.50 2 4 3.0 0 0 1.990 1.99 1.000 0.100 #NAME? 35 33 #NAME? 0.590 1.15 35.06 20.700 0.250 100.0 1 1 1 1 1 1 0 110 80 9 4 20.0 16.00 13.00
271 271 0 30 55.1 167.640 #NAME? 11 72 18 12.00 2 5 8.5 0 0 127.040 1.99 7.860 2.230 #NAME? 34 32 #NAME? 1.540 4.1 24.01 22.100 0.250 83.0 1 1 1 1 1 1 0 110 80 7 6 19.0 18.00 8.00
272 272 0 26 55.7 162.000 #NAME? 17 72 18 11.00 2 6 1.0 0 0 164.910 1.99 6.120 3.250 #NAME? 36 33 #NAME? 4.750 3.2 15.63 31.600 0.250 84.0 0 0 0 0 1 0 1 110 70 2 3 18.0 18.00 8.70
273 273 0 43 40.0 152.000 #NAME? 15 72 18 11.50 2 6 25.0 0 0 1.990 1.99 14.990 7.090 #NAME? 30 28 #NAME? 6.090 0.16 24.78 24.500 0.300 100.0 0 0 0 0 1 0 1 120 70 4 4 17.0 18.00 8.30
274 274 0 30 52.0 152.000 #NAME? 13 78 20 11.10 2 4 6.0 1 0 785.440 1.99 6.300 6.260 #NAME? 32 30 #NAME? 2.580 8.1 38.24 26.300 0.250 112.0 0 0 0 1 1 0 1 120 80 8 7 18.0 16.00 7.00
275 275 0 30 58.9 153.000 #NAME? 13 72 18 12.00 2 5 4.0 0 0 1.990 1.99 5.590 3.850 #NAME? 35 33 #NAME? 3.000 0.56 128.24 30.300 0.350 88.0 0 0 0 0 0 0 1 120 80 4 6 17.0 17.00 10.30
276 276 1 27 47.0 152.000 #NAME? 15 74 20 10.70 2 5 8.0 0 0 355.080 1.99 6.800 4.610 #NAME? 33 28 #NAME? 3.490 5.3 14.65 26.100 0.250 90.0 0 0 0 0 0 0 1 120 70 12 15 16.0 18.00 8.70
277 277 1 36 65.7 151.000 #NAME? 15 72 20 11.00 4 4 8.0 0 1 89.340 89.34 7.470 6.640 #NAME? 37 35 #NAME? 5.000 1.8 8.00 19.900 0.250 92.0 1 0 0 1 1 1 0 120 80 6 5 18.0 16.00 8.80
278 278 1 29 66.0 150.000 #NAME? 15 72 18 12.00 4 5 9.0 0 0 1.990 1.99 2.560 2.410 #NAME? 36 34 #NAME? 2.860 7.3 12.91 19.000 0.390 92.0 0 1 1 1 0 0 1 110 70 1 1 12.0 14.00 9.50
279 279 1 45 50.0 154.000 #NAME? 17 72 18 10.20 4 4 11.0 0 2 412.900 366.04 2.240 1.990 #NAME? 30 27 #NAME? 22.590 6.5 16.84 10.500 0.500 138.0 1 1 1 1 1 1 0 120 80 4 8 15.0 18.00 7.20
280 280 0 26 54.0 150.000 #NAME? 11 72 18 12.00 4 3 5.0 0 0 184.800 1.99 5.810 3.910 #NAME? 36 34 #NAME? 2.350 21 29.69 20.100 0.480 125.0 1 1 1 1 0 1 0 120 70 3 2 15.0 13.00 9.00
281 281 0 31 65.0 158.000 #NAME? 15 72 18 12.40 2 5 4.0 0 0 1.990 1.99 2.950 0.260 #NAME? 38 36 #NAME? 3.230 0.9 17.19 25.500 0.660 92.0 0 1 0 1 1 0 1 120 70 3 3 18.0 15.00 8.90
282 282 0 31 51.0 155.000 #NAME? 17 70 18 11.00 4 5 5.0 0 0 1.990 1.99 60.370 9.990 #NAME? 33 30 #NAME? 1.000 15.3 20.47 25.600 0.330 92.0 0 0 0 0 1 0 1 110 80 4 5 16.0 19.00 7.00
283 283 1 38 62.0 154.000 #NAME? 15 74 20 10.50 4 4 5.0 1 0 454.270 14.34 8.150 3.260 #NAME? 35 33 #NAME? 7.020 10.6 22.90 16.700 0.480 92.0 1 0 0 1 1 1 0 120 80 8 5 15.0 17.00 9.80
284 284 0 32 68.0 158.000 #NAME? 17 72 18 10.50 2 6 13.0 0 0 3.100 75.62 2.350 0.290 #NAME? 37 35 #NAME? 5.000 4.2 38.51 24.000 0.700 100.0 1 0 0 0 1 1 1 110 80 3 5 17.0 16.00 9.80
285 285 0 32 46.0 152.000 #NAME? 15 72 20 11.50 2 6 8.0 0 0 1.990 1.99 4.790 4.500 #NAME? 32 30 #NAME? 2.000 4.7 96.63 22.600 0.500 112.0 0 0 0 1 1 0 0 120 80 1 1 11.0 16.00 11.00
286 286 0 38 52.0 156.000 #NAME? 17 72 18 10.80 2 5 10.0 0 0 227.560 4.96 3.750 0.340 #NAME? 33 30 #NAME? 1.260 4.8 17.07 20.100 0.300 110.0 0 0 0 1 0 0 1 120 80 1 2 20.0 17.00 7.20
287 287 0 26 67.0 154.000 #NAME? 11 72 18 13.20 2 5 5.0 0 1 1.990 1.99 7.760 2.710 #NAME? 36 34 #NAME? 3.710 1.7 23.98 17.100 0.250 122.0 1 0 0 1 0 1 0 120 70 2 1 16.0 14.00 7.80
288 288 1 29 53.0 152.000 #NAME? 17 78 22 11.00 2 5 2.5 0 0 53.090 1.99 6.060 2.140 #NAME? 34 32 #NAME? 2.350 5.4 12.26 18.000 0.250 100.0 0 1 1 0 1 0 0 110 80 10 7 18.0 14.00 9.00
289 289 0 35 66.0 155.000 #NAME? 15 78 22 10.50 4 3 4.0 0 2 1.990 1.99 6.510 3.230 #NAME? 37 35 #NAME? 4.610 1.2 18.14 27.200 0.310 100.0 1 0 0 0 0 1 0 110 80 1 0 15.0 18.00 7.70
290 290 0 27 65.0 162.000 #NAME? 15 72 18 10.70 2 5 3.5 0 1 213.210 1.99 5.470 4.950 #NAME? 38 36 #NAME? 4.290 5.4 41.03 21.400 0.250 92.0 1 1 1 1 1 0 0 110 80 5 3 16.0 17.00 8.00
291 291 1 27 83.0 164.000 #NAME? 13 70 18 11.00 2 6 2.0 0 0 1.990 1.99 4.600 2.490 #NAME? 39 36 #NAME? 6.480 17.5 12.88 26.600 0.500 92.0 0 0 0 1 1 0 0 110 80 1 1 14.0 15.00 8.50
292 292 0 35 53.0 158.000 #NAME? 17 74 20 12.40 2 6 11.0 0 1 1.990 1.99 8.990 4.880 #NAME? 35 33 #NAME? 1.320 6 17.00 30.500 0.400 110.0 1 0 0 0 1 1 0 120 80 1 3 16.0 17.00 9.00
293 293 0 30 68.0 158.000 #NAME? 15 72 18 10.50 2 4 10.0 0 0 1.990 1.99 1.760 0.200 #NAME? 37 35 #NAME? 0.940 2 43.63 35.800 0.410 92.0 0 1 1 1 0 0 1 120 70 2 2 12.0 16.00 10.50
294 294 1 27 36.0 153.000 #NAME? 13 70 18 11.40 2 5 6.5 1 0 1134.400 1134.4 4.250 5.110 #NAME? 30 28 #NAME? 2.080 21.8 19.31 23.300 0.410 79.0 1 1 1 0 1 1 0 110 80 11 12 13.0 15.00 5.50
295 295 1 28 55.5 144.000 #NAME? 13 72 20 11.80 2 5 6.5 1 2 785.950 785.95 4.000 2.950 #NAME? 38 35 #NAME? 0.860 18.5 7.49 20.400 0.620 95.0 1 1 1 0 1 1 0 120 80 13 20 14.0 16.00 9.60
296 296 1 30 57.0 155.000 #NAME? 13 72 18 12.00 2 5 10.0 0 0 291.430 1.99 5.470 3.000 #NAME? 35 33 #NAME? 6.540 12.4 39.60 23.900 0.940 106.0 0 0 0 0 0 1 1 110 70 9 10 18.0 19.00 8.00
297 297 0 31 50.0 155.000 #NAME? 15 13 18 11.00 2 4 12.0 0 0 20.810 1.99 6.698 5.130 #NAME? 33 30 #NAME? 3.861 10.8 14.02 24.900 0.410 100.0 0 0 1 0 1 0 0 110 70 8 5 17.0 15.00 8.50
298 298 0 20 52.0 150.000 #NAME? 15 72 18 10.50 2 6 3.0 0 0 1.990 1.99 4.870 3.610 #NAME? 34 32 #NAME? 2.930 4.5 20.18 27.000 0.340 88.0 0 0 0 1 1 1 0 110 70 7 9 13.0 17.00 9.60
299 299 0 34 67.0 152.000 #NAME? 15 74 20 11.50 2 6 13.0 0 1 1.990 1.99 5.590 3.050 #NAME? 37 35 #NAME? 3.670 4.2 14.88 13.900 0.700 95.0 1 1 1 1 1 1 0 120 70 1 0 16.0 12.00 7.70
300 300 0 26 50.0 152.000 #NAME? 11 72 20 11.00 4 5 2.0 0 0 1.990 1.99 5.700 4.500 #NAME? 32 28 #NAME? 1.240 1.2 38.70 23.200 0.260 92.0 0 0 1 0 0 1 1 120 80 1 0 15.0 18.00 11.00
301 301 1 40 85.0 173.000 #NAME? 13 72 18 10.50 2 5 13.0 0 1 1.990 1.99 4.380 1.640 #NAME? 38 36 #NAME? 3.740 10.8 29.76 18.300 0.330 100.0 1 0 0 0 1 1 0 110 80 2 1 18.0 15.00 7.00
302 302 0 28 53.0 152.000 #NAME? 11 72 18 11.20 2 4 7.5 0 1 110.780 1.99 5.710 2.830 #NAME? 33 30 #NAME? 4.690 10 22.10 30.300 0.400 100.0 1 1 1 1 0 1 0 110 80 7 5 16.0 13.00 9.00
303 303 0 26 64.0 153.000 #NAME? 17 72 18 12.00 4 3 3.0 0 0 1.990 1.99 5.140 8.170 #NAME? 34 31 #NAME? 1.100 18.7 31.83 30.000 0.460 135.0 0 0 0 1 0 0 1 120 80 3 4 16.0 14.00 9.50
304 304 1 25 57.0 148.000 #NAME? 15 72 20 10.00 2 5 1.0 0 0 1.990 1.9 2.520 1.410 #NAME? 30 28 #NAME? 3.040 18 35.03 31.200 0.560 80.0 1 1 1 1 1 0 0 110 80 10 12 15.0 14.00 9.00
305 305 0 35 52.0 150.000 #NAME? 13 70 18 10.70 2 5 7.0 0 0 1.990 1.99 5.040 2.220 #NAME? 34 32 #NAME? 0.810 0.28 16.40 25.000 0.330 94.0 0 0 0 0 1 0 1 110 80 3 2 17.0 15.00 12.00
306 306 0 37 56.0 152.000 #NAME? 13 74 20 11.70 2 5 9.0 0 0 42.000 1.99 2.910 0.350 #NAME? 35 33 #NAME? 16.000 a 2.22 38.600 0.300 100.0 0 0 0 0 1 0 1 120 70 4 5 17.0 16.00 5.60
307 307 1 41 40.0 155.000 16.6 13 72 16 11.00 2 5 7.0 0 0 1.990 1.99 3.680 0.830 4.433 34 32 0.941 1.130 1.03 7.49 33.100 0.250 82.0 0 1 1 0 1 0 0 110 70 8 12 11.0 11.50 5.50
308 308 0 24 68.0 158.000 27.2 15 72 16 10.80 2 6 3.0 1 0 229.860 229.86 3.330 1.990 1.673 39 36 0.923 2.000 3.02 10.57 22.500 0.250 80.0 1 0 0 0 1 1 0 120 80 2 4 12.5 12.00 8.00
309 309 1 33 52.0 152.000 22.5 11 72 18 11.00 2 5 8.0 0 1 1.990 1.99 5.840 4.540 1.286 38 32 0.842 3.740 1 33.68 31.550 0.250 92.0 0 1 1 1 1 1 0 110 70 6 9 13.0 13.00 6.00
310 310 1 24 52.0 162.000 19.8 15 72 18 11.00 4 3 4.0 0 0 1.990 1.99 3.260 1.660 #NAME? 39 32 #NAME? 2.410 1.5 9.64 28.400 0.780 92.0 0 0 1 0 1 1 0 110 80 6 7 15.0 16.00 7.00
311 311 0 39 55.0 158.000 22 15 72 20 12.00 2 5 14.0 0 0 3.900 3.9 7.270 2.480 #NAME? 38 34 #NAME? 5.000 3 38.15 30.500 0.250 95.0 1 0 0 0 1 0 1 110 70 1 2 15.0 11.00 7.30
312 312 1 30 72.0 161.000 27.8 11 72 22 12.20 4 2 1.5 1 0 297.210 297.21 11.620 2.060 #NAME? 45 38 #NAME? 2.130 1 14.06 32.400 0.380 108.0 1 1 1 1 1 1 0 110 80 12 6 17.0 18.00 12.00
313 313 1 28 56.0 158.000 22.4 15 74 20 12.00 2 5 7.0 1 0 277.280 277.28 4.890 1.970 #NAME? 39 32 #NAME? 1.570 1.06 19.04 34.100 0.460 100.0 1 0 1 0 1 1 0 120 70 7 12 15.0 18.00 11.00
314 314 0 38 50.0 161.000 19.1 15 72 22 12.00 2 5 13.0 1 0 1.990 783.36 3.710 2.440 #NAME? 38 32 #NAME? 0.540 3.05 53.39 42.500 0.290 100.0 0 0 0 0 1 1 1 110 80 7 9 11.0 15.00 7.80
315 315 1 34 55.0 158.000 22 11 78 20 11.00 2 6 10.0 0 0 1.990 1.99 6.470 5.570 #NAME? 36 32 #NAME? 3.010 5.4 24.70 35.800 0.350 100.0 0 1 1 0 1 1 0 120 80 5 5 12.0 10.00 11.00
316 316 0 28 65.0 167.000 23.3 11 72 18 9.00 2 6 5.0 1 0 21084.210 21084.21 3.760 0.620 #NAME? 38 34 #NAME? 1.820 3.02 28.56 52.400 0.250 120.0 0 0 0 0 0 1 0 110 80 8 12 14.0 18.00 8.50
317 317 0 30 66.0 152.000 28.6 11 72 18 12.00 2 5 7.0 1 0 409.850 409.85 3.400 1.310 #NAME? 40 36 #NAME? 1.680 2.23 10.45 36.500 0.360 110.0 1 0 0 1 1 1 0 110 80 8 4 16.0 17.00 10.00
318 318 0 28 53.0 159.000 21 11 74 18 10.80 2 6 4.0 1 0 17243.970 410.13 4.890 0.580 #NAME? 36 30 #NAME? 1.140 2.06 21.19 22.200 0.270 80.0 0 0 0 1 1 1 0 100 70 4 6 14.0 18.00 7.70
319 319 0 47 60.0 164.500 22.2 13 72 18 13.00 2 5 24.0 1 0 479.600 479.6 4.590 1.400 #NAME? 36 32 #NAME? 5.890 3.33 13.78 35.400 0.410 100.0 0 0 0 1 1 1 0 110 80 6 6 15.0 18.00 8.40
320 320 0 32 60.0 154.000 25.3 15 72 18 13.80 2 5 5.0 1 0 545.780 545.78 6.030 3.240 #NAME? 38 34 #NAME? 3.280 1 22.14 25.100 0.380 85.0 1 0 0 1 1 1 0 120 80 1 2 16.0 17.00 6.40
321 321 1 33 64.0 162.000 24.4 13 72 18 10.20 2 5 13.0 1 0 783.980 1.99 3.170 1.450 #NAME? 36 30 #NAME? 0.820 2.65 18.13 33.500 0.250 82.0 0 1 1 1 1 1 0 110 80 7 20 15.0 17.00 12.00
322 322 0 38 50.0 150.000 22.2 17 72 18 12.00 4 3 12.0 0 0 1.990 1.99 4.300 2.620 #NAME? 34 30 #NAME? 2.420 11 32.23 29.400 0.260 80.0 0 0 0 1 1 1 0 110 80 5 11 8.0 12.00 10.50
323 323 0 28 61.0 150.000 27.1 11 72 24 10.80 2 6 6.5 0 0 1.990 1.99 7.030 2.300 #NAME? 39 35 #NAME? 0.220 3.6 26.89 32.400 0.350 85.0 1 0 0 0 1 1 0 110 80 7 11 15.0 18.00 8.00
324 324 1 37 56.0 155.000 23.3 17 78 22 12.00 4 2 14.0 1 0 320.490 320.49 2.060 0.790 #NAME? 37 34 #NAME? 4.620 5.7 63.30 35.700 0.250 110.0 1 0 1 1 1 1 0 110 80 14 19 18.0 19.00 12.00
325 325 0 40 64.0 158.000 25.6 13 74 28 10.80 2 5 17.0 0 0 1.990 1.99 6.200 5.000 #NAME? 39 35 #NAME? 7.270 1.03 35.66 27.600 0.310 100.0 1 0 0 0 1 1 1 110 80 4 7 10.0 16.00 9.00
326 326 0 35 57.0 158.000 22.8 15 80 22 10.60 2 6 17.0 1 0 12.000 12 4.080 1.320 #NAME? 36 32 #NAME? 4.020 6.33 26.01 23.400 0.290 85.0 0 0 0 1 1 1 1 110 70 4 4 10.0 15.00 11.00
327 327 0 45 54.0 152.000 23.4 13 72 16 10.60 2 6 23.0 0 3 3.010 78.38 5.270 3.050 #NAME? 38 35 #NAME? 3.690 2.5 22.29 37.900 0.480 100.0 0 0 0 1 1 0 1 120 80 2 4 10.0 15.00 13.00
328 328 0 31 55.0 161.000 21.2 11 74 18 10.20 2 5 11.0 1 0 204.690 204.69 3.830 4.790 #NAME? 37 34 #NAME? 0.450 2.2 20.32 32.600 0.340 85.0 0 0 0 1 1 0 1 110 70 4 3 18.0 13.00 9.00
329 329 1 30 65.0 161.000 25.1 11 70 18 11.00 4 2 7.0 1 0 336.950 12 6.860 14.240 #NAME? 39 34 #NAME? 2.320 15.7 8.78 18.700 0.320 100.0 1 1 1 0 1 1 0 110 80 7 10 13.0 17.00 8.00
330 330 0 29 53.0 161.000 20.4 15 72 18 10.20 2 6 5.0 1 0 900.600 900.6 5052.000 3.680 #NAME? 38 35 #NAME? 0.830 3.5 30.58 28.600 0.300 108.0 0 0 0 1 1 1 0 110 80 6 4 16.0 17.00 6.00
331 331 0 33 60.0 162.000 22.9 11 72 18 10.30 2 6 13.0 0 0 1.990 1.99 5.890 1.270 #NAME? 38 35 #NAME? 0.690 1.1 1.36 20.300 0.250 100.0 0 0 0 1 1 0 0 120 80 1 2 12.0 18.00 9.00
332 332 0 29 59.0 155.000 24.6 17 70 18 10.70 4 3 12.0 1 0 12.000 12 6.470 1.470 #NAME? 38 34 #NAME? 1.570 1.3 34.14 32.400 0.250 92.0 0 0 0 0 0 1 0 110 80 3 3 13.0 10.00 6.00
333 333 0 27 59.0 158.000 23.6 15 72 18 10.00 2 6 6.0 1 0 159.710 159.71 1.850 0.670 #NAME? 36 32 #NAME? 2.130 3 23.15 34.500 0.250 92.0 0 0 0 1 1 0 0 110 70 3 3 14.0 12.00 10.50
334 334 0 31 45.0 153.000 19.2 13 80 20 10.80 2 5 8.0 1 0 15.000 15 3.910 2.510 #NAME? 35 30 #NAME? 2.240 3 23.20 47.500 0.250 100.0 0 0 1 1 0 0 0 110 80 4 1 11.0 10.00 12.00
335 335 0 30 62.0 152.000 26.8 15 74 18 11.50 2 5 6.0 1 0 296.310 296.31 0.210 0.920 #NAME? 38 35 #NAME? 1.750 2.17 12.11 22.900 0.420 77.0 1 0 0 1 1 1 0 120 80 6 7 15.0 11.00 11.00
336 336 0 30 46.0 161.000 17.7 15 78 22 10.80 2 5 5.0 1 0 8012.990 4176 2.440 0.220 #NAME? 34 31 #NAME? 0.790 3.3 14.98 13.400 0.890 100.0 0 0 0 1 1 0 0 110 80 2 2 14.0 15.00 9.00
337 337 1 21 82.0 158.000 32.8 16 78 24 10.60 4 3 3.0 1 0 1.990 183.06 3.410 1.390 #NAME? 44 38 #NAME? 5.000 14.7 31.70 33.700 0.250 85.0 1 1 1 1 1 1 0 110 80 8 10 15.0 12.00 10.00
338 338 1 36 89.0 161.000 34.3 15 78 24 14.00 4 3 5.0 0 0 1.990 1.99 7.100 2.220 #NAME? 44 40 #NAME? 4.230 2.8 10.16 35.100 0.250 130.0 1 1 1 1 1 1 0 110 80 16 12 11.0 9.00 9.00
339 339 1 31 73.0 161.000 28.2 15 78 24 10.70 4 2 7.0 0 0 1.990 1.99 3.610 5.090 #NAME? 42 38 #NAME? 1.690 1.06 14.11 29.100 0.790 92.0 1 1 1 0 0 1 0 110 80 12 10 10.0 10.00 9.30
340 340 0 29 53.0 152.000 22.9 17 78 22 10.60 2 5 2.0 1 0 1.990 20 5.710 4.320 #NAME? 38 34 #NAME? 3.980 9.1 20.78 19.700 0.320 123.0 0 0 1 0 0 1 1 110 80 1 1 10.0 12.00 8.20
341 341 1 26 60.0 152.000 26 13 78 22 12.50 4 2 5.0 1 0 161.770 161.77 6.830 4.620 #NAME? 40 36 #NAME? 2.390 5.9 34.20 38.600 0.300 76.0 1 1 0 0 1 1 0 100 80 10 15 14.0 11.00 10.00
342 342 1 26 65.0 158.000 26 15 75 24 10.50 2 5 7.0 1 0 61.980 61.98 7.120 7.890 #NAME? 40 35 #NAME? 2.290 8.9 32.41 23.400 0.330 107.0 1 1 0 0 1 1 0 110 80 10 7 10.0 11.00 8.70
343 343 1 32 40.0 158.000 16 15 78 22 11.20 2 5 12.0 1 0 403.850 403.85 6.720 3.240 #NAME? 36 32 #NAME? 1.770 3.6 13.27 57.700 0.250 107.0 0 0 0 1 1 1 0 110 80 4 15 15.0 12.00 11.00
344 344 0 25 85.0 161.000 32.8 11 78 22 10.20 2 5 3.0 1 2 136.710 1.99 7.100 5.160 #NAME? 42 38 #NAME? 2.150 2.31 19.21 31.400 0.250 100.0 1 0 0 1 0 1 0 110 80 10 5 15.0 17.00 8.00
345 345 0 36 62.0 158.000 24.8 13 78 22 11.00 2 6 14.0 1 0 34.650 34.65 4.480 1.250 #NAME? 40 36 #NAME? 0.720 3.6 8.38 30.100 0.250 107.0 0 0 0 1 1 1 0 110 80 3 5 10.0 11.00 9.20
346 346 0 30 50.0 152.000 21.6 15 78 22 10.50 4 3 2.0 1 0 48.860 48.86 4.630 0.300 #NAME? 38 35 #NAME? 1.130 2.8 5.65 36.900 0.250 92.0 0 0 0 0 1 1 0 110 80 1 1 10.0 10.00 13.00
347 347 1 28 83.0 164.000 30.9 11 74 20 11.10 4 2 7.0 0 1 1.990 1.99 5.500 3.030 #NAME? 42 37 #NAME? 20.850 20.4 14.63 10.500 0.520 110.0 1 1 0 1 1 1 0 110 80 10 12 15.0 17.00 10.00
348 348 0 31 56.0 158.000 22.4 11 72 18 10.20 2 6 10.0 1 0 371.740 371.74 1.800 0.350 #NAME? 38 35 #NAME? 0.530 1.5 20.90 34.000 0.250 100.0 0 0 0 1 1 1 0 110 70 6 4 10.0 11.00 11.00
349 349 0 32 57.0 157.000 23.1 13 72 20 10.80 2 5 4.0 0 0 1.990 1.99 3.980 1.430 #NAME? 38 34 #NAME? 4.290 4.6 15.39 26.100 0.450 80.0 0 0 1 0 1 1 1 110 70 4 8 11.0 15.00 10.80
350 350 0 30 71.0 157.000 28.8 13 72 18 11.00 4 2 5.0 0 0 1.990 1.99 7.570 4.650 #NAME? 42 36 #NAME? 1.420 5 20.45 13.900 0.590 92.0 1 0 0 1 1 1 0 110 80 1 3 12.0 15.00 14.00
351 351 0 22 56.0 154.000 23.6 13 72 18 10.80 2 6 1.0 1 0 213.490 1.99 4.690 3.670 #NAME? 39 34 #NAME? 3.620 3.9 25.50 35.500 1.240 100.0 0 0 0 1 1 1 0 110 80 10 6 15.0 12.00 9.00
352 352 1 36 54.0 148.000 24.7 13 78 24 10.20 4 2 15.0 1 0 970.750 970.75 5.260 9.870 #NAME? 38 33 #NAME? 1.450 9.8 8.10 42.100 0.480 85.0 0 1 0 1 1 1 0 100 80 10 7 13.0 15.00 9.00
353 353 1 27 60.0 164.000 22.3 15 72 18 10.70 2 6 2.5 1 0 109.060 109.06 3.190 2.960 #NAME? 40 35 #NAME? 2.960 3.65 18.92 36.400 0.250 100.0 0 1 1 0 0 1 0 120 80 4 5 15.0 18.00 8.00
354 354 0 30 55.0 164.000 20.4 13 74 22 10.20 2 6 7.0 1 1 1082.820 1082.82 5.740 2.810 #NAME? 37 34 #NAME? 1.840 3.5 24.52 31.200 0.250 92.0 0 0 0 1 1 1 0 110 80 6 4 13.0 15.00 5.60
355 355 0 33 50.0 152.000 21.6 15 80 20 9.80 4 2 12.0 1 0 739.130 739.13 6.680 4.030 #NAME? 38 35 #NAME? 2.380 10.7 24.52 45.400 0.320 85.0 0 0 0 1 1 1 0 110 80 7 4 15.0 18.00 10.60
356 356 0 28 61.0 158.000 24.4 15 72 18 10.50 2 6 6.0 1 0 288.720 288.72 4.160 0.270 #NAME? 40 35 #NAME? 6.310 4.8 54.88 17.400 0.250 100.0 0 1 0 0 1 0 1 110 80 5 7 15.0 18.00 7.00
357 357 1 32 80.4 165.000 29.5 18 72 18 11.50 4 3 10.0 0 0 4.680 1.99 4.480 0.210 #NAME? 42 38 #NAME? 2.060 4.5 48.57 19.300 0.270 105.0 1 1 1 0 1 1 0 120 80 9 12 15.0 15.00 11.00
358 358 0 32 47.0 158.000 18.8 15 74 20 10.20 2 6 7.0 1 0 18.490 18.49 4.520 3.270 #NAME? 37 33 #NAME? 1.400 2.6 19.78 18.800 1.000 115.0 0 1 0 0 1 0 0 110 80 1 1 14.0 16.00 6.40
359 359 0 34 53.0 145.000 25.2 12 78 20 10.80 2 6 10.0 1 0 169.330 169.33 3.270 0.630 #NAME? 38 35 #NAME? 1.460 3.9 31.77 23.900 0.400 115.0 1 0 1 0 0 1 0 120 70 2 2 10.0 15.00 12.00
360 360 0 36 61.0 154.000 25.7 13 70 18 10.50 2 6 12.0 1 0 418.900 418.9 2.850 1.710 #NAME? 38 35 #NAME? 1.540 3.09 26.09 20.160 0.310 95.0 1 0 0 1 1 0 1 120 80 2 3 13.0 11.00 6.80
361 361 0 34 61.0 154.000 25.7 15 72 18 10.50 2 5 7.0 0 0 1.990 1.99 6.420 3.450 #NAME? 39 34 #NAME? 2.640 10.9 14.52 39.100 0.250 100.0 1 0 0 1 0 0 0 120 70 3 3 13.0 14.00 13.50
362 362 0 35 62.0 148.000 28.3 13 78 20 10.80 2 6 17.0 0 1 1.990 1.99 2.710 1.410 #NAME? 40 36 #NAME? 1.870 5.2 28.27 30.900 0.300 92.0 1 0 0 1 1 0 1 120 70 2 3 10.0 10.00 7.00
363 363 0 42 50.0 161.000 19.3 15 74 16 10.00 4 3 5.0 1 0 14.830 53.82 4.910 0.670 #NAME? 37 32 #NAME? 0.970 1.6 23.58 21.300 0.250 82.0 0 0 0 1 0 0 0 120 80 1 1 14.0 13.00 9.20
364 364 0 31 48.0 152.000 20.8 17 72 18 11.00 2 6 8.0 0 0 1.990 1.99 5.980 1.000 #NAME? 35 30 #NAME? 3.130 1 20.83 43.400 0.560 100.0 0 0 0 1 1 1 0 110 80 1 3 13.0 13.00 7.00 7
365 365 0 35 48.0 158.000 19.2 15 72 20 10.80 2 5 10.0 1 1 650.710 1.99 7.770 3.640 #NAME? 36 30 #NAME? 1.450 6.4 29.56 24.600 0.550 110.0 0 0 1 0 0 1 0 120 80 3 3 16.0 15.00 9.00
366 366 0 38 77.9 161.000 30.1 15 72 24 11.10 2 5 2.0 0 0 1.990 1.99 4.320 1.350 #NAME? 42 37 #NAME? 1.120 2.8 19.20 20.500 0.340 100.0 1 1 0 0 1 1 0 140 100 2 2 15.0 14.00 8.50
367 367 1 23 67.0 152.000 29 11 72 20 10.00 4 2 2.0 0 0 1.990 1.99 3.230 2.340 #NAME? 38 32 #NAME? 1.270 6.4 22.99 15.900 0.320 92.0 1 0 1 1 1 0 0 120 80 8 11 13.0 15.00 9.80
368 368 0 20 64.0 165.000 23.5 15 72 18 11.50 2 6 8.0 1 0 10.450 10.45 2.080 0.150 #NAME? 38 35 #NAME? 5.000 2.19 50.51 28.700 0.250 86.0 0 0 0 1 1 1 0 120 80 5 7 15.0 14.00 11.00
369 369 1 22 58.0 161.000 22.4 13 74 22 10.00 4 3 5.0 1 0 764.830 764.83 4.060 3.920 #NAME? 38 34 #NAME? 5.000 15 37.49 22.000 0.560 92.0 0 1 0 1 1 1 0 120 80 8 11 15.0 14.00 10.00
370 370 0 35 60.0 152.000 26 15 74 18 10.00 2 5 3.0 1 0 116.310 116.31 6.520 3.420 #NAME? 39 36 #NAME? 0.770 2.5 40.73 20.000 0.300 92.0 1 0 0 1 0 1 0 110 80 3 4 12.0 15.00 8.00
371 371 1 28 58.0 164.000 21.6 13 72 18 11.50 2 6 1.0 1 0 785.420 300.13 6.220 4.440 #NAME? 37 34 #NAME? 0.640 4.2 27.24 26.000 0.290 100.0 0 1 1 0 1 1 1 110 80 15 12 18.0 20.00 12.00
372 372 0 31 68.0 161.000 26.2 15 72 18 12.80 2 6 6.0 1 0 96.220 5.39 1.580 0.240 #NAME? 39 35 #NAME? 6.910 5.1 15.85 25.600 0.500 87.0 1 0 0 1 1 1 0 110 80 4 9 12.0 17.00 9.00
373 373 1 29 74.0 159.000 29.3 11 72 20 10.20 4 3 3.0 1 1 346.590 346.59 5.760 6.420 #NAME? 40 37 #NAME? 0.950 6 45.54 36.200 0.320 92.0 1 1 1 0 0 1 0 120 80 12 9 17.0 19.00 8.00
374 374 0 28 42.0 163.000 15.8 13 70 18 12.00 2 5 7.0 1 0 347.980 1.99 2.590 0.630 #NAME? 35 30 #NAME? 5.740 5.8 33.04 39.300 0.395 84.0 0 0 0 1 1 0 0 120 70 8 7 15.0 15.00 11.00
375 375 0 32 65.0 161.000 25.1 11 74 20 10.20 2 6 11.0 1 0 6.190 6.19 2.170 2.020 #NAME? 38 34 #NAME? 4.780 0.2 99.93 17.300 0.250 115.0 1 0 0 0 1 1 0 110 70 1 2 13.0 14.00 7.50
376 376 1 27 60.0 158.000 24 14 72 20 11.20 2 4 8.0 1 0 187.790 187.79 5.400 3.070 #NAME? 36 32 #NAME? 6.750 9 40.43 23.100 0.460 100.0 0 1 0 0 1 0 1 120 80 9 10 14.0 12.00 10.30
377 377 0 35 60.0 152.000 26 11 72 18 10.00 2 6 10.0 1 0 459.630 459.63 3.910 1.010 #NAME? 38 35 #NAME? 2.450 1.2 8.60 20.700 0.300 115.0 0 0 1 0 1 1 0 120 80 1 2 18.0 11.00 9.00
378 378 0 28 61.0 152.000 26.4 15 74 22 10.50 4 3 3.0 0 0 2.000 3.01 3.300 0.590 #NAME? 39 35 #NAME? 2.780 9.1 7.81 10.100 0.270 92.0 1 0 0 0 1 1 0 120 80 8 7 19.0 15.00 5.50
379 379 0 23 50.0 155.000 20.8 15 72 18 10.80 4 6 3.0 0 0 2.000 2 9.590 5.980 #NAME? 37 34 #NAME? 3.630 2.9 30.85 21.100 0.750 92.0 0 0 0 1 1 0 0 110 70 3 5 18.0 18.00 11.20
380 380 0 31 55.5 151.000 24.3 15 74 18 11.10 2 6 5.0 1 0 278.320 278.52 2.490 3.390 #NAME? 38 34 #NAME? 1.620 3.1 24.33 24.100 0.250 92.0 0 0 1 0 1 1 0 110 80 6 7 18.0 22.00 9.00
381 381 0 37 65.0 161.000 25.1 15 72 20 10.30 2 5 6.0 0 0 1.990 1.99 6.460 1.430 #NAME? 38 34 #NAME? 1.440 5.6 19.32 24.100 0.250 82.0 1 0 0 0 1 1 0 120 80 6 6 12.0 15.00 10.00
382 382 0 35 60.0 146.000 28.1 15 82 20 10.00 4 3 10.0 0 0 1.990 1.99 2.750 2.780 #NAME? 37 35 #NAME? 1.570 7.9 15.38 21.700 0.250 110.0 1 0 0 1 0 1 0 120 80 5 5 13.0 15.00 6.50
383 383 0 29 46.0 150.000 20.4 11 72 20 10.80 2 6 6.0 1 1 10.840 10.84 5.080 1.060 #NAME? 34 33 #NAME? 2.660 18.9 52.31 40.300 0.290 110.0 0 0 0 1 1 1 0 120 80 8 5 16.0 12.00 7.00
384 384 0 32 60.5 158.000 24.2 13 72 18 11.00 2 6 9.0 0 2 1.990 1.99 4.610 1.520 #NAME? 38 34 #NAME? 1.400 11.4 25.54 20.100 0.250 100.0 1 0 0 0 1 0 1 120 80 2 3 18.0 20.00 8.00
385 385 0 37 50.0 148.000 22.8 15 72 18 10.20 2 7 5.0 1 0 184.420 184.42 4.410 1.430 #NAME? 37 33 #NAME? 2.730 3.8 18.93 19.500 0.440 100.0 0 0 0 1 0 1 0 110 80 5 3 15.0 17.00 7.50
386 386 0 41 76.9 165.000 28.2 15 72 18 11.00 4 3 14.0 0 0 4.200 4.2 6.520 6.650 #NAME? 42 38 #NAME? 0.580 4.5 19.81 18.600 0.480 100.0 1 0 1 1 0 1 0 130 80 4 2 14.0 11.00 7.00
387 387 0 40 63.0 171.000 21.5 15 72 18 11.00 2 6 17.0 0 0 1.990 1.99 1.940 0.490 #NAME? 38 34 #NAME? 5.000 2 42.66 20.800 0.790 138.0 0 0 1 0 0 1 0 110 70 3 1 11.0 12.00 8.30
388 388 1 24 60.0 152.000 26 11 74 16 11.00 2 5 8.0 1 0 305.760 1.99 5.970 2.730 #NAME? 38 32 #NAME? 3.300 4.5 27.96 19.300 0.250 125.0 1 0 1 1 1 1 0 110 70 16 14 12.0 15.00 7.50
389 389 0 31 55.5 151.000 24.3 15 74 18 11.10 2 5 5.0 1 0 278.320 278.32 2.490 3.390 #NAME? 37 34 #NAME? 1.620 3.1 24.33 24.100 0.250 92.0 0 0 0 0 1 1 1 110 80 6 7 18.0 12.00 9.00
390 390 0 23 50.0 155.000 20.8 15 72 18 10.80 4 2 3.0 0 0 2.000 2 9.590 5.980 #NAME? 36 32 #NAME? 3.630 2.9 30.85 21.100 0.750 92.0 0 0 0 1 0 0 1 110 70 3 5 15.0 12.00 11.20
391 391 1 28 61.0 152.000 26.4 15 74 20 10.50 4 2 3.0 0 0 2.000 3.01 3.300 0.590 #NAME? 38 35 #NAME? 2.780 9.1 7.81 10.100 0.270 92.0 1 0 0 1 1 1 0 120 80 10 9 19.0 15.00 10.00
392 392 0 35 60.0 152.000 26 11 72 18 10.00 2 6 10.0 1 0 459.630 459.63 3.910 1.010 #NAME? 39 36 #NAME? 2.450 1.2 8.60 20.700 0.300 115.0 1 0 0 1 0 0 0 120 80 1 2 15.0 11.00 9.00
393 393 1 27 45.0 158.000 18 14 72 20 11.20 2 5 8.0 1 0 187.790 187.79 5.400 3.070 #NAME? 36 32 #NAME? 6.750 9 40.43 23.100 0.460 100.0 0 0 1 0 0 1 1 120 80 10 11 15.0 13.00 10.30
394 394 0 32 65.0 161.000 25.1 11 74 20 10.20 2 5 11.0 1 0 6.190 6.19 2.170 2.020 #NAME? 38 35 #NAME? 4.780 0.2 99.93 17.300 0.250 115.0 1 0 0 0 0 1 0 110 70 1 2 13.0 15.00 7.50
395 395 0 28 42.0 163.000 15.8 13 70 18 12.00 2 6 7.0 1 0 347.980 1.99 2.590 0.630 #NAME? 35 30 #NAME? 5.740 5.8 33.04 39.300 0.395 84.0 0 0 1 0 1 1 0 120 70 8 7 16.0 15.00 11.00
396 396 1 29 74.0 159.000 29.3 11 72 20 10.80 4 3 3.0 1 0 346.590 346.59 5.760 6.420 #NAME? 42 38 #NAME? 0.950 6 45.54 36.200 0.320 92.0 1 0 1 1 1 1 0 120 80 10 9 15.0 14.00 8.00
397 397 0 31 68.0 161.000 26.2 15 72 18 12.80 2 6 6.0 1 0 96.220 5.39 1.580 0.240 #NAME? 40 37 #NAME? 6.910 5.1 15.85 25.600 0.500 87.0 1 0 0 1 1 0 0 110 80 4 9 12.0 15.00 7.00
398 398 0 35 60.0 152.000 26 15 74 18 10.00 2 6 3.0 1 0 116.310 116.31 6.520 3.420 #NAME? 38 34 #NAME? 0.770 2.5 40.73 20.000 0.300 92.0 1 0 0 0 1 0 1 110 80 3 4 12.0 11.00 8.00
399 399 1 36 60.0 154.000 25.3 11 72 18 11.20 2 4 5.0 1 1 232.640 232.64 3.720 1.600 #NAME? 38 35 #NAME? 1.600 1.6 18.35 24.600 0.480 92.0 1 1 0 0 1 1 0 110 80 6 9 19.0 17.00 5.80
400 400 0 30 67.0 165.000 24.6 13 80 20 10.00 2 6 5.0 1 0 167.410 167.41 5.730 2.420 #NAME? 39 35 #NAME? 2.310 1.9 25.13 12.400 0.580 100.0 0 0 0 1 0 1 0 120 70 3 3 18.0 19.00 9.60
401 401 0 27 58.0 161.000 22.4 13 74 18 10.50 2 5 8.0 0 0 1.990 1.99 2.100 1.080 #NAME? 37 34 #NAME? 3.400 1.14 10.76 25.400 0.250 100.0 0 0 0 1 1 0 0 120 70 4 4 14.0 16.00 7.00
402 402 0 28 65.6 159.000 25.9 11 72 20 10.50 2 6 5.0 0 0 2.000 2 4.440 1.030 #NAME? 39 36 #NAME? 3.670 5.7 23.65 15.500 0.580 130.0 1 0 0 1 0 1 0 120 80 5 5 14.0 16.00 8.00
403 403 1 28 65.0 158.000 25 11 74 20 10.50 4 3 4.5 1 0 230.530 230.53 5.180 6.020 #NAME? 36 32 #NAME? 1.570 28.6 12.62 21.200 0.250 125.0 1 0 1 1 1 1 0 110 70 11 12 19.0 17.00 11.00
404 404 1 27 64.0 160.000 25 13 78 22 10.80 2 5 3.0 0 0 2.000 2 3.070 0.550 #NAME? 36 32 #NAME? 2.280 5.5 28.89 25.100 0.250 80.0 1 0 1 1 0 1 0 120 80 4 11 18.0 20.00 7.00
405 405 0 26 38.0 148.000 17.3 15 72 18 10.50 2 6 3.0 1 0 10.400 10.4 2.720 2.530 #NAME? 28 25 #NAME? 1.500 3.8 42.01 47.800 0.460 130.0 0 0 1 0 0 0 0 120 70 4 5 18.0 20.00 9.50
406 406 0 33 50.0 158.000 20 15 74 18 10.20 2 5 2.0 1 0 465.010 465.01 3.390 2.770 #NAME? 35 31 #NAME? 2.770 4.2 19.06 43.300 0.310 82.0 0 0 0 1 1 1 0 120 80 2 3 14.0 24.00 6.80
407 407 0 33 45.0 142.000 22.3 11 72 20 11.50 2 6 9.0 0 0 1.990 1.99 2.390 0.890 #NAME? 35 31 #NAME? 1.670 0.5 38.45 23.700 0.280 85.0 0 0 0 1 0 1 0 120 80 1 3 17.0 20.00 9.00
408 408 0 36 59.0 155.000 24.6 15 74 20 10.00 2 5 17.0 0 0 2.000 2 5.210 1.780 #NAME? 38 34 #NAME? 1.870 0.9 22.83 25.900 0.380 100.0 0 0 0 0 1 1 0 120 80 2 3 17.0 18.00 11.00
409 409 0 45 57.0 160.000 22.3 15 72 22 10.30 2 6 23.0 0 0 1.990 1.99 4.080 3.600 #NAME? 38 35 #NAME? 1.960 4.8 20.19 17.400 0.400 130.0 0 0 1 0 1 1 0 110 70 3 6 14.0 12.00 9.00
410 410 0 40 60.0 161.000 23.1 13 74 20 11.20 2 6 13.0 1 0 41.750 41.75 5.130 2.970 #NAME? 38 35 #NAME? 2.780 1.3 11.81 16.100 0.250 146.0 0 0 0 1 1 0 0 110 70 3 0 10.0 11.00 4.00
411 411 0 34 54.7 158.000 21.9 13 72 20 10.40 2 5 9.0 1 0 363.080 8.54 6.710 8.710 #NAME? 37 33 #NAME? 3.650 6.4 12.31 25.800 4.250 100.0 0 0 0 1 1 1 1 120 80 1 1 13.0 15.00 8.00
412 412 0 40 53.0 150.000 23.6 15 78 20 11.40 2 5 14.0 1 0 382.360 382.36 4.800 3.830 #NAME? 34 30 #NAME? 1.980 8.1 8.10 44.100 0.250 115.0 0 0 0 1 0 1 0 120 70 3 4 2.0 13.00 11.00
413 413 1 23 55.0 152.000 23.2 13 78 18 10.80 2 5 3.0 0 0 1.990 1.99 40.080 1.990 #NAME? 36 32 #NAME? 1.350 10.8 43.94 29.400 0.400 108.0 0 1 1 1 1 0 1 110 70 10 18 14.0 14.00 10.30
414 414 0 26 52.0 150.000 23.1 15 74 20 10.00 2 5 9.0 0 0 1.990 1.99 3.520 2.140 #NAME? 35 31 #NAME? 0.740 5.2 15.59 30.100 0.300 115.0 0 0 0 1 1 1 0 110 70 1 1 13.0 14.00 13.00
415 415 1 30 64.0 158.000 25.6 11 74 22 10.20 2 4 2.2 1 0 2229.810 10.98 4.750 7.200 #NAME? 38 35 #NAME? 4.750 7.2 32.06 33.800 0.250 92.0 1 0 1 0 1 1 0 110 70 12 14 14.0 14.00 9.50
416 416 1 31 60.0 160.000 23.5 15 80 20 10.80 4 2 7.0 1 0 252.720 252.72 1.800 0.900 #NAME? 39 35 #NAME? 1.860 15 41.94 23.600 0.250 92.0 0 1 1 0 1 1 0 120 80 8 12 12.0 16.00 6.50
417 417 0 26 45.0 149.000 20.3 13 74 20 10.20 2 5 8.0 1 0 67.200 67.2 7.140 3.790 #NAME? 32 28 #NAME? 1.000 3.4 10.80 28.100 0.250 100.0 0 0 0 1 0 1 0 120 80 6 2 14.0 13.00 8.00
418 418 0 32 71.8 162.000 27.4 11 72 18 10.80 2 6 4.0 0 0 1.990 1.99 3.680 0.130 #NAME? 41 38 #NAME? 2.640 2.28 20.93 27.100 0.250 92.0 1 0 1 0 1 0 1 120 80 3 3 14.0 13.00 9.00
419 419 1 27 65.0 160.000 25.4 15 74 20 11.20 2 4 2.0 0 0 1.990 1.99 3.850 1.020 #NAME? 37 34 #NAME? 2.390 2.6 9.34 15.600 0.250 110.0 1 0 1 0 1 1 0 110 80 4 9 10.0 15.00 6.70
420 420 0 36 58.0 154.000 24.5 15 72 18 11.00 2 5 12.0 0 0 1.990 1.99 4.750 2.460 #NAME? 36 32 #NAME? 0.950 4.8 26.39 25.400 0.350 92.0 0 0 0 1 1 1 0 110 80 3 3 13.0 15.00 7.80
421 421 1 25 45.6 152.000 19.7 11 74 20 11.00 2 5 1.0 1 0 6.921 6.921 5.120 3.600 #NAME? 30 26 #NAME? 1.880 11.9 20.52 29.300 0.250 115.0 0 1 0 1 1 1 0 120 80 14 10 15.0 15.00 10.00
422 422 0 40 74.1 164.000 27.6 15 78 20 11.50 2 6 18.0 0 0 1.990 1.99 1.000 0.600 #NAME? 42 38 #NAME? 4.450 6.9 26.35 17.100 0.350 92.0 1 0 1 0 1 1 1 120 80 2 4 10.0 16.00 7.20
423 423 1 38 68.0 164.000 25.3 15 74 20 10.40 4 2 1.0 1 0 482.210 482.21 7.100 4.270 #NAME? 38 35 #NAME? 3.980 0.1 6.18 25.300 0.370 107.0 1 0 1 1 1 1 0 120 80 13 12 15.0 15.00 8.20
424 424 0 42 55.0 150.000 24.4 13 74 20 10.70 2 5 5.0 1 0 40.220 1.99 2.970 0.200 #NAME? 36 32 #NAME? 3.580 12.8 37.90 23.100 0.300 107.0 0 0 0 1 1 1 0 120 80 4 5 14.0 10.00 11.00
425 425 0 29 51.0 158.000 20.4 15 72 20 11.00 2 5 7.0 1 0 50.110 0.99 2.010 1.810 #NAME? 32 28 #NAME? 1.340 2.7 71.77 23.500 0.250 100.0 0 0 0 0 0 1 1 110 80 6 3 12.0 11.00 8.50
426 426 1 37 59.0 148.000 26.9 11 74 20 10.80 4 3 4.0 0 0 1.990 1.99 7.610 4.550 #NAME? 36 32 #NAME? 3.510 6 13.62 26.400 0.290 92.0 1 0 1 0 1 1 0 120 80 8 12 17.0 0.17 11.20
427 427 0 33 64.0 158.000 25.6 13 74 20 11.00 2 5 12.0 1 1 103.500 103.5 3.350 0.560 #NAME? 36 32 #NAME? 2.980 0.84 25.97 23.800 0.250 106.0 1 0 0 1 0 0 1 110 80 7 3 19.0 17.00 9.00
428 428 1 32 63.0 158.000 25.2 11 72 18 10.60 4 2 10.0 1 1 102.870 102.87 3.270 2.300 #NAME? 36 31 #NAME? 0.860 2.9 67.97 18.100 0.380 115.0 1 0 1 1 1 0 0 110 70 3 11 19.0 17.00 8.90
429 429 1 28 48.0 148.000 21.9 11 74 20 10.20 2 6 4.0 1 0 90.670 90.67 4.000 1.610 #NAME? 30 26 #NAME? 1.150 4.6 64.47 34.400 0.250 100.0 0 0 1 1 1 1 0 110 70 11 9 19.0 15.00 11.50
430 430 1 33 60.0 152.000 26 13 72 18 12.50 2 5 6.0 1 0 375.180 375.18 2.500 1.170 #NAME? 38 34 #NAME? 2.530 20 25.37 35.100 0.250 92.0 1 0 1 1 1 1 0 130 80 14 16 16.0 16.00 10.20
431 431 1 25 65.0 154.000 27.4 13 72 20 11.00 4 3 5.0 1 0 728.010 728.01 4.590 2.340 #NAME? 36 32 #NAME? 2.380 8.9 24.84 18.700 0.460 72.0 1 0 1 0 0 0 1 120 80 4 6 13.0 14.00 7.60
432 432 0 42 65.0 158.000 26 11 72 18 10.50 2 6 2.0 1 0 433.970 433.97 6.760 1.760 #NAME? 38 35 #NAME? 3.505 1.2 12.40 22.700 0.350 115.0 1 0 0 0 1 1 0 110 70 5 7 14.0 15.00 7.00
433 433 0 29 50.0 148.000 22.8 15 72 22 11.00 2 5 5.0 0 0 1.990 1.99 5.640 2.710 #NAME? 32 28 #NAME? 1.240 2.8 15.31 32.700 0.250 225.0 0 0 0 1 1 0 0 120 70 4 6 10.0 13.00 7.00
434 434 0 33 57.0 163.000 21.5 15 72 18 11.50 2 6 10.0 0 0 1.990 1.99 4.090 2.630 #NAME? 32 28 #NAME? 1.080 10.3 10.62 27.200 0.250 100.0 0 0 0 1 1 1 1 110 70 5 7 13.0 11.00 10.00
435 435 1 23 70.0 158.000 28 13 72 18 11.00 2 5 2.0 0 0 1.990 1.99 4.500 1.500 #NAME? 42 38 #NAME? 2.040 9.9 17.50 18.000 0.500 92.0 1 0 1 1 1 1 0 110 70 8 11 14.0 15.00 11.00
436 436 0 30 64.0 170.000 22.1 15 72 18 11.10 2 5 6.0 0 0 1399.000 1.99 3.750 1.570 #NAME? 40 36 #NAME? 1.510 2.5 27.72 31.800 0.250 94.0 0 0 0 1 1 0 1 110 80 4 3 11.0 16.00 9.00
437 437 0 29 48.0 150.000 21.3 15 70 18 10.20 2 6 6.0 1 0 213.140 213.14 13.900 0.500 #NAME? 32 27 #NAME? 0.820 3.6 30.98 74.500 0.800 92.0 0 0 0 1 0 1 0 110 80 4 4 14.0 13.00 10.00
438 438 1 32 71.0 157.000 28.8 13 74 20 10.50 4 3 13.0 0 0 4.200 4.2 6.550 4.300 #NAME? 42 38 #NAME? 2.270 8 18.94 36.600 0.250 85.0 1 0 1 1 1 1 0 120 80 9 12 18.0 19.00 8.00
439 439 1 33 76.0 164.000 28.3 11 72 20 11.00 4 2 6.0 1 1 86.420 238.14 3.470 3.130 #NAME? 42 39 #NAME? 3.900 1.01 30.53 32.900 0.380 100.0 1 0 1 0 1 1 0 110 80 11 9 16.0 19.00 10.00
440 440 1 34 73.0 161.000 28.2 13 72 20 11.20 2 5 3.0 1 0 70.530 1.99 4.540 1.360 #NAME? 40 36 #NAME? 2.330 10.2 15.26 18.200 0.250 130.0 1 0 0 1 1 1 0 110 80 7 13 15.0 19.00 10.30
441 441 0 26 56.0 160.000 20.3 11 70 20 11.10 2 6 3.0 1 0 1.990 181.23 5.710 2.420 #NAME? 34 30 #NAME? 1.460 11 21.87 16.900 0.250 110.0 0 0 0 1 0 1 1 110 70 9 6 19.0 14.00 8.00
442 442 0 31 64.0 170.000 22.1 15 74 18 11.00 4 3 6.0 0 0 3.350 1.99 1.060 1.490 #NAME? 36 32 #NAME? 3.020 0.6 10.43 12.000 0.860 115.0 0 0 0 0 1 0 1 110 80 3 5 11.0 12.00 3.00
443 443 0 40 56.0 158.000 22.4 15 72 18 12.80 2 6 22.0 1 2 407.250 491.85 5.650 3.310 #NAME? 34 30 #NAME? 3.850 0.5 29.50 24.300 0.540 107.0 0 0 0 1 0 0 1 110 70 6 2 9.0 11.00 7.00
444 444 1 39 55.0 150.000 24.4 15 78 20 10.70 4 7 13.0 1 0 12.370 19.44 5.400 1.950 #NAME? 38 36 #NAME? 1.000 0.37 21.88 18.600 0.460 94.0 0 0 0 0 1 1 0 120 80 12 11 19.0 19.00 8.10
445 445 1 27 52.0 150.000 23.1 11 82 20 11.10 2 5 5.0 0 0 1.990 1.99 4.970 2.230 #NAME? 36 32 #NAME? 1.940 16.9 9.12 23.600 0.250 93.0 0 0 0 0 0 1 1 120 80 1 1 16.0 17.00 8.55
446 446 1 26 72.0 150.000 32 15 72 20 12.00 4 2 3.0 0 0 144.630 1.99 3.630 8.340 #NAME? 42 40 #NAME? 2.920 26.4 29.72 22.400 0.380 93.0 1 0 1 0 1 1 0 110 80 8 7 20.0 20.00 6.00
447 447 0 29 63.0 162.000 24 11 72 18 13.20 2 5 11.0 0 0 30004.000 475.04 1.990 1.610 #NAME? 39 37 #NAME? 1.770 5.96 21.95 22.700 0.460 95.0 0 1 1 0 0 1 0 110 80 6 7 18.0 19.00 5.90
448 448 1 47 62.7 160.000 24.5 15 72 18 10.00 4 7 30.0 0 0 30007.000 1.99 1.880 0.250 #NAME? 39 36 #NAME? 2.470 6.2 31.47 12.100 0.250 92.0 0 0 0 0 1 1 1 110 80 14 11 20.0 19.00 6.00
449 449 0 41 71.5 160.000 27.9 15 74 20 11.00 4 7 16.0 1 1 1.900 1.99 4.740 3.600 #NAME? 42 39 #NAME? 1.090 9.1 22.62 31.600 0.250 85.0 1 1 1 0 0 1 0 120 80 9 7 19.0 18.00 8.00
450 450 1 33 63.0 159.000 24.9 15 72 18 10.10 4 2 7.0 1 0 294.060 1.99 5.020 12.040 #NAME? 39 37 #NAME? 3.150 0.8 33.23 40.200 0.300 100.0 0 0 0 0 1 1 0 120 80 6 8 19.0 17.00 8.20
451 451 1 34 69.0 167.640 24.6 15 80 20 11.50 2 5 10.0 0 0 53.510 13.12 4.070 1.930 #NAME? 40 38 #NAME? 3.080 0.91 25.61 61.300 0.320 107.0 1 1 0 1 1 1 0 110 80 20 16 18.0 14.00 7.00
452 452 1 30 44.0 161.544 16.9 12 72 18 11.40 2 4 4.0 1 0 595.800 1.92 8.250 0.460 #NAME? 28 24 #NAME? 3.510 0.98 26.42 30.500 0.240 84.0 0 0 0 0 0 0 1 100 70 5 18 11.0 21.00 9.65
453 453 1 31 50.0 158.000 20 11 74 20 10.80 2 5 11.0 0 0 482.870 1.99 3.070 0.500 #NAME? 36 30 #NAME? 1.330 0.89 19.51 29.200 0.250 92.0 0 0 1 0 1 1 1 110 80 10 19 17.0 18.00 8.50
454 454 1 35 57.0 154.000 24 15 72 18 10.70 2 5 7.0 0 0 950.410 1.99 4.410 1.970 #NAME? 38 32 #NAME? 5.000 0.87 12.71 16.000 0.270 92.0 0 0 0 0 0 0 1 110 70 11 15 19.0 21.00 7.85
455 455 1 35 61.0 158.000 24.4 13 72 18 10.50 2 5 13.0 0 1 355.510 1.99 3.710 2.670 #NAME? 38 33 #NAME? 2.970 9.7 20.35 15.000 0.290 93.0 0 0 1 0 1 1 1 120 80 12 13 19.0 18.00 8.20
456 456 1 32 52.0 150.000 23.1 13 70 18 9.40 2 5 12.0 0 0 272.780 1.99 4.330 2018.000 #NAME? 36 32 #NAME? 2.100 7.7 40.47 41.040 0.250 93.0 0 0 0 0 1 0 1 110 80 6 7 18.0 18.00 9.00
457 457 1 31 51.0 156.000 21 17 78 20 11.10 2 6 5.0 0 0 148.520 1.99 4.960 1.750 #NAME? 36 31 #NAME? 2.200 1.1 12.11 25.700 0.350 93.0 0 0 1 0 1 0 1 120 70 14 10 17.0 17.00 7.00
458 458 1 31 38.0 158.496 15.1 15 80 20 12.00 4 2 3.0 0 0 1.920 1.99 3.990 6.630 #NAME? 26 25 #NAME? 1.300 17.6 13.91 19.500 0.250 133.0 0 0 0 0 1 0 0 120 70 7 6 17.0 19.00 8.00
459 459 1 36 66.0 162.000 25.1 15 72 20 11.00 4 7 NA 0 0 515.330 1.99 1.640 0.170 #NAME? 39 37 #NAME? 1.860 6.6 25.00 20.800 0.250 93.0 0 0 0 0 0 0 0 120 80 14 5 19.0 19.00 8.00
460 460 0 36 55.0 154.000 23.2 13 74 18 10.00 2 5 8.0 0 0 8.000 1.99 3.220 0.100 #NAME? 38 37 #NAME? 2.910 5 22.84 53.210 0.260 92.0 0 0 0 1 0 1 0 110 80 4 1 22.0 14.00 10.50
461 461 0 27 63.0 158.000 25.2 12 72 18 11.20 2 4 2.5 0 1 152.670 1.99 7.900 3.360 #NAME? 39 37 #NAME? 2.280 7 13.11 42.120 0.250 80.0 0 0 0 0 0 1 0 110 70 5 8 18.0 19.00 7.35
462 462 0 33 55.0 158.496 21.9 15 72 22 12.00 2 5 6.0 1 0 480.200 1.9 3.820 1.380 #NAME? 38 36 #NAME? 2.160 9 32.37 19.800 0.240 92.0 0 0 0 0 0 1 0 110 80 0 4 0.0 20.00 8.90
463 463 1 33 89.0 163.000 33.5 12 78 22 10.80 4 7 6.0 1 0 381.990 15 3.450 0.720 #NAME? 46 40 #NAME? 0.730 0.7 15.23 16.700 0.280 100.0 1 0 1 1 1 1 0 120 80 13 12 19.0 18.00 7.90
464 464 0 41 45.0 160.000 17.6 14 78 22 11.00 2 5 18.0 1 0 152.900 268.37 4.410 1.270 #NAME? 38 37 #NAME? 2.190 10 22.61 25.456 0.300 100.0 0 1 1 1 1 1 0 110 80 2 1 19.0 18.00 7.20
465 465 1 34 54.0 153.000 23.1 16 72 20 11.00 4 7 3.5 0 0 598.080 1.99 2.240 2.300 #NAME? 36 35 #NAME? 6.660 0.85 60.40 46.210 0.250 90.0 0 0 0 0 0 0 0 110 80 21 19 16.0 18.00 7.60
466 466 0 33 50.0 150.000 22.2 15 70 18 10.80 2 4 8.0 0 0 194.390 1.99 2.580 0.200 #NAME? 36 34 #NAME? 1.790 6 20.83 58.320 0.450 90.0 0 0 0 0 0 0 0 110 80 6 9 20.0 18.00 8.00
467 467 0 35 61.8 153.000 26.4 17 70 20 12.00 2 4 11.0 0 0 1.990 1.99 6.750 3.230 #NAME? 39 38 #NAME? 2.280 6 10.61 62.500 0.480 100.0 0 0 0 0 0 1 0 120 80 11 9 21.0 16.00 8.00
468 468 1 32 64.0 162.000 24.4 12 72 18 11.50 4 2 9.0 1 0 561.720 1.99 3.580 3.550 #NAME? 39 34 #NAME? 0.580 0.78 25.37 15.300 0.360 102.0 1 0 0 0 0 0 0 110 70 13 12 19.0 19.00 7.00
469 469 0 40 56.0 148.000 25.6 12 72 18 10.50 2 5 7.0 1 1 10.000 1.99 6.560 3.440 #NAME? 38 34 #NAME? 5.000 16 32.46 21.600 0.250 92.0 0 0 1 0 0 1 1 110 80 9 10 22.0 17.00 8.15
470 470 1 25 55.0 154.000 23.2 15 74 20 12.00 2 5 4.0 0 0 10.000 1.99 4.670 0.710 #NAME? 38 36 #NAME? 1.820 0.74 24.81 60.265 0.350 102.0 0 0 0 1 0 0 1 110 70 12 12 24.0 18.00 8.90
471 471 1 34 52.0 149.000 23.4 13 80 20 12.00 4 7 10.0 0 2 1.980 8 5.480 6.370 #NAME? 36 32 #NAME? 2.930 0.91 19.21 12.300 0.340 100.0 0 1 1 1 0 1 1 110 80 8 10 17.0 20.00 7.00
472 472 1 31 50.0 152.000 21.6 13 80 20 11.00 2 5 7.0 0 0 381.900 1.99 6.210 1.000 #NAME? 36 34 #NAME? 1.750 0.99 32.06 18.500 0.250 90.0 0 0 0 1 0 1 0 110 70 5 14 15.0 20.00 7.50
473 473 1 26 68.0 164.592 25.1 11 72 18 12.00 2 5 6.0 1 1 10.000 9.35 5.050 2.010 #NAME? 42 38 #NAME? 1.270 0.9 14.85 32.800 0.240 92.0 1 0 0 0 1 1 0 110 70 12 14 19.0 18.00 8.95
474 474 0 39 50.0 154.000 21.1 15 78 22 12.10 2 5 16.0 0 0 1.970 1.99 3.800 2.000 #NAME? 34 30 #NAME? 4.350 4.13 14.13 23.700 0.250 100.0 0 0 0 0 0 0 0 120 80 5 7 19.0 20.00 5.00
475 475 0 48 55.0 151.000 24.1 15 70 18 9.40 4 2 25.0 0 0 4983.210 127.2 4.080 2.600 #NAME? 38 36 #NAME? 0.450 4.02 27.72 29.200 0.300 91.0 0 0 0 0 1 0 1 130 80 9 10 18.0 19.00 9.00
476 476 1 31 55.0 156.000 22.6 13 72 20 12.10 2 4 7.0 1 0 1.990 56.4 6.530 7.480 #NAME? 38 32 #NAME? 2.310 6.09 14.79 30.800 0.250 84.0 0 0 1 0 0 0 1 120 80 6 15 18.0 20.00 8.50
477 477 0 32 61.0 165.000 22.4 17 78 20 11.50 2 5 1.5 1 0 1.970 1.99 9.140 3.370 #NAME? 40 35 #NAME? 0.580 0.71 20.07 36.300 0.640 116.0 0 0 0 0 0 0 1 110 80 2 1 20.0 17.00 6.40
478 478 1 42 94.0 155.448 38.9 16 74 18 10.70 4 7 16.0 0 0 501.310 1.99 5.590 2.020 #NAME? 48 47 #NAME? 1.910 3.62 7.38 28.200 0.550 91.0 1 1 1 1 0 1 0 130 80 18 11 20.0 19.00 9.20
479 479 0 28 60.0 160.000 23.4 15 72 18 11.70 2 5 4.0 0 0 21.450 168.99 8.250 2.840 #NAME? 39 37 #NAME? 1.000 0.89 33.24 26.300 0.390 91.0 0 0 0 0 0 0 0 120 80 3 5 18.0 17.00 6.00
480 480 0 27 60.0 158.496 23.9 11 72 20 10.40 2 5 7.0 1 0 696.830 1.99 5.860 2.370 #NAME? 39 38 #NAME? 1.000 1.97 15.82 25.300 0.400 116.0 0 0 1 1 1 0 0 110 80 5 8 18.0 18.00 9.00
481 481 0 26 52.0 164.592 19.2 13 70 18 10.00 2 5 3.0 0 0 22.000 1.99 6.800 0.430 #NAME? 36 35 #NAME? 1.470 1.4 49.01 24.500 0.250 84.0 0 0 0 0 0 0 0 110 80 7 5 20.0 21.00 8.00
482 482 0 44 66.1 148.000 30.8 13 70 20 10.40 2 5 8.0 0 0 1.890 1.99 7.810 2.020 #NAME? 40 34 #NAME? 3.250 3.03 13.79 27.500 0.400 125.0 1 0 0 0 1 0 0 130 80 5 9 16.0 19.00 4.50
483 483 0 32 43.0 161.544 16.5 13 72 22 12.10 2 5 7.0 1 0 1.990 1.99 5.510 4.100 #NAME? 34 30 #NAME? 2.270 3.86 40.29 32.600 0.370 108.0 0 0 0 0 1 1 1 110 80 1 3 19.0 19.00 8.60
484 484 1 33 87.9 158.000 35.2 15 72 20 10.00 4 2 8.0 0 0 70.350 1.99 2.810 0.370 #NAME? 46 44 #NAME? 6.550 5.75 28.05 21.000 0.210 116.0 1 0 0 0 1 1 0 120 80 7 12 20.0 14.00 8.00
485 485 0 31 63.0 156.000 25.9 11 72 20 10.80 2 5 5.0 0 0 1.990 1.99 5.280 3.460 #NAME? 39 36 #NAME? 2.480 6.26 28.08 16.250 25.300 116.0 0 1 0 1 0 1 1 120 80 9 10 18.0 17.00 7.25
486 486 0 30 44.8 157.000 18.2 15 80 20 10.00 2 5 9.0 1 0 1238.400 237.5 1.000 0.100 #NAME? 34 30 #NAME? 3.260 0.72 44.42 33.300 0.250 91.0 0 0 0 0 0 1 1 110 80 2 4 15.0 18.00 8.50
487 487 1 30 49.1 159.000 19.4 11 74 18 11.70 4 2 4.0 1 0 710.400 1.99 7.590 6.690 #NAME? 36 32 #NAME? 3.580 10.53 22.73 21.700 0.390 116.0 0 1 1 0 0 1 1 110 80 16 16 19.0 19.00 10.50
488 488 0 36 62.0 152.000 26.8 15 74 20 12.10 4 2 9.0 0 3 1.970 1.99 3.350 5.060 #NAME? 40 37 #NAME? 1.750 10.32 24.63 27.600 0.340 84.0 1 1 0 0 0 1 0 120 70 4 4 18.0 19.00 6.75
489 489 0 36 76.9 165.000 28.2 11 72 18 10.80 2 5 7.0 0 0 37.190 1.99 5.650 2.010 #NAME? 42 41 #NAME? 6.960 2.39 16.64 22.000 0.250 116.0 1 0 0 0 0 1 0 110 80 5 9 20.0 19.00 6.80
490 490 1 28 63.0 143.000 30.8 11 72 18 11.80 2 4 11.0 0 0 1.990 1.99 7.090 2.340 #NAME? 40 39 #NAME? 0.270 2.7 44.36 37.600 0.370 84.0 1 0 0 0 0 0 0 120 80 7 15 16.0 16.00 10.00
491 491 0 35 61.0 154.000 25.7 12 80 20 10.20 4 7 16.0 0 0 1.990 1.99 4.620 1.230 #NAME? 40 38 #NAME? 1.170 1.1 54.86 27.200 0.500 92.0 1 1 1 1 1 1 0 120 70 3 2 19.0 16.00 6.00
492 492 0 23 66.0 164.592 24.4 16 72 18 11.50 4 2 3.0 1 0 658.290 1.99 8.130 1.390 #NAME? 38 37 #NAME? 4.140 4.9 27.20 36.100 0.250 100.0 0 0 0 0 1 1 0 110 80 6 5 18.0 20.00 7.20
493 493 0 25 58.0 155.448 24 14 72 18 10.80 2 5 3.0 1 0 1.990 1.99 5.680 4.210 #NAME? 34 32 #NAME? 2.760 10.6 24.01 19.300 0.480 92.0 0 1 0 1 0 0 0 120 80 1 2 19.0 18.00 10.35
494 494 1 30 50.0 154.000 21.1 15 72 20 10.80 2 4 6.0 1 0 492.020 1.99 7.730 0.260 #NAME? 34 33 #NAME? 8.300 4.3 13.14 25.700 0.250 85.0 0 0 0 0 0 0 1 120 80 9 19 19.0 18.00 6.00
495 495 0 30 48.0 148.000 21.9 15 80 20 10.50 2 5 6.0 1 0 341.470 1.99 2.440 0.350 #NAME? 28 27 #NAME? 1.470 5.1 13.38 38.200 0.251 92.0 1 0 1 0 1 1 0 110 70 5 8 18.0 18.00 7.00
496 496 1 27 64.0 155.000 26.6 13 72 20 11.50 4 2 5.0 0 0 275.600 1.99 5.740 1.290 #NAME? 40 36 #NAME? 1.510 16.8 27.51 23.400 0.610 140.0 1 1 1 1 1 1 0 120 70 10 11 18.0 16.00 6.40
497 497 0 34 45.0 150.000 20 17 70 18 10.70 2 5 8.0 0 0 336.510 1.99 3.100 3.480 #NAME? 28 26 #NAME? 2.170 7.9 14.08 27.100 0.400 103.0 0 0 0 0 0 0 0 110 80 5 4 18.0 16.00 5.40
498 498 0 35 54.0 140.000 27.6 11 72 20 10.80 2 5 5.0 1 0 5.650 1.99 4.620 1.330 #NAME? 36 34 #NAME? 4.070 3.1 27.17 31.600 0.380 112.0 0 0 0 0 1 0 0 110 70 9 7 20.0 16.00 6.60
499 499 0 23 52.0 148.000 23.7 15 72 20 11.00 2 5 4.0 0 0 1.990 1.99 7.720 4.100 #NAME? 32 30 #NAME? 5.000 5.1 12.60 25.900 0.500 92.0 0 0 0 0 0 0 0 120 80 1 1 7.0 17.00 6.00
500 500 0 28 65.0 163.000 24.5 14 74 20 10.80 2 5 3.0 0 0 296.570 1.99 6.500 10.500 #NAME? 32 31 #NAME? 1.830 11.2 34.16 33.000 0.300 100.0 0 0 0 0 0 0 0 120 80 8 7 18.0 20.00 8.80
501 501 0 40 59.0 152.000 25.5 13 72 18 11.50 2 5 10.0 1 1 1.990 1.99 3.870 1.070 #NAME? 44 42 #NAME? 2.920 1.7 7.12 34.200 0.490 120.0 1 1 0 1 0 0 0 100 70 3 5 17.0 18.00 14.20
502 502 0 38 70.0 164.592 25.8 16 72 18 12.10 2 4 8.0 0 0 1.990 1.99 3.500 0.200 #NAME? 46 42 #NAME? 3.100 5.2 24.68 32.200 0.450 92.0 1 1 0 0 1 0 0 110 70 2 7 19.0 19.00 9.00
503 503 0 38 52.0 150.000 23.1 15 80 20 11.00 2 5 15.0 0 0 469.550 1.99 6.810 5.750 #NAME? 36 35 #NAME? 1.220 2.2 37.56 25.300 0.250 100.0 0 0 0 0 0 0 0 120 80 3 1 21.0 19.00 9.30
504 504 0 31 52.9 153.000 22.6 16 70 20 11.00 2 5 9.0 0 0 147.900 1.99 7.160 4.210 #NAME? 34 32 #NAME? 4.520 1.3 23.29 15.100 0.350 85.0 0 0 0 0 0 0 0 120 80 3 3 17.0 18.00 6.80
505 505 0 33 48.0 162.000 18.3 17 72 18 10.20 2 5 5.0 0 0 6.000 10.24 7.050 4.510 #NAME? 28 24 #NAME? 8.470 12 22.29 28.600 0.220 100.0 0 0 0 0 0 0 0 110 80 7 10 18.0 16.00 10.00
506 506 0 35 52.0 152.000 22.5 13 74 20 11.10 2 4 15.0 1 1 1.990 1.99 2.200 0.510 #NAME? 30 29 #NAME? 2.460 3.3 40.41 24.000 0.310 115.0 0 0 0 0 0 0 0 110 70 3 3 18.0 18.00 7.00
507 507 0 35 42.0 152.400 18.1 11 78 18 10.80 2 5 12.0 0 1 300.530 1.99 1.680 0.300 #NAME? 28 24 #NAME? 3.070 1.7 32.87 15.000 0.490 108.0 0 0 1 1 0 0 0 120 80 3 10 16.0 21.00 7.00
508 508 0 37 62.0 153.000 26.5 11 74 20 10.70 2 4 13.0 0 0 316.490 1.99 8.060 3.280 #NAME? 44 43 #NAME? 1.740 0.9 10.91 24.300 0.330 107.0 0 1 0 1 0 1 0 110 80 6 6 19.0 19.00 7.50
509 509 0 36 54.3 150.000 24.1 11 78 20 10.00 2 5 12.0 0 0 1.990 1.99 7.400 3.970 #NAME? 32 30 #NAME? 2.120 4.9 20.84 17.900 0.250 138.0 0 1 0 0 0 0 0 110 70 1 1 16.0 19.00 9.10
510 510 1 46 54.0 152.000 23.4 13 74 20 14.00 2 4 16.0 1 1 856.110 1.99 2.850 0.370 #NAME? 30 28 #NAME? 1.130 16.6 8.10 23.700 0.290 92.0 1 0 0 0 0 1 0 120 70 20 18 18.0 17.00 7.80
511 511 0 24 57.0 158.496 22.7 17 70 20 11.20 2 5 3.0 0 0 1.990 1.99 1.720 0.430 #NAME? 32 30 #NAME? 4.020 17.6 25.99 44.800 0.700 100.0 1 0 0 0 0 0 0 120 70 8 10 18.0 19.00 8.40
512 512 0 44 56.0 142.000 27.8 13 72 18 11.80 2 5 3.0 0 0 42.610 1.99 2.810 0.150 #NAME? 38 36 #NAME? 1.010 4.6 10.89 16.900 0.690 82.0 0 0 0 0 1 0 0 110 80 9 5 19.0 19.00 6.00
513 513 1 23 72.0 165.000 26.4 13 72 18 10.60 5 7 5.0 0 0 121.050 1.99 2.130 0.570 #NAME? 42 40 #NAME? 1.000 5.5 16.33 17.000 0.250 92.0 0 1 1 0 0 1 0 120 70 8 10 16.0 18.00 9.30
514 514 0 21 55.0 158.496 21.9 16 72 18 11.50 2 5 3.0 0 0 1.990 1.99 8.550 2.530 #NAME? 40 38 #NAME? 1.790 2.2 17.23 13.800 0.230 100.0 0 0 0 0 0 0 0 120 80 1 3 7.0 19.00 8.50
515 515 0 32 65.0 170.688 22.3 16 72 18 10.50 2 5 6.0 0 0 171.850 1.99 7.450 2.860 #NAME? 38 36 #NAME? 2.970 18.9 21.04 30.000 0.490 100.0 0 0 0 0 0 1 0 110 70 9 7 18.0 18.00 9.00
516 516 0 31 69.0 164.000 25.7 15 72 18 11.00 2 5 4.0 0 0 1.990 1.99 6.540 2.310 #NAME? 46 44 #NAME? 1.410 10 19.19 22.900 0.300 92.0 0 0 0 1 0 0 0 110 80 10 8 20.0 17.00 7.00
517 517 0 34 50.0 152.000 21.6 15 72 18 10.00 2 5 8.0 0 0 1.990 104.87 3.230 2.700 #NAME? 32 28 #NAME? 3.920 16 7.16 21.700 0.250 92.0 0 0 1 0 0 0 0 100 70 12 10 19.0 16.00 9.20
518 518 0 27 59.0 152.000 25.2 11 72 18 11.50 2 5 2.0 0 0 320.070 1.99 5.950 3.900 #NAME? 36 34 #NAME? 2.210 5.4 35.19 24.200 0.500 92.0 0 0 0 0 0 0 0 110 70 5 8 20.0 21.00 9.80
519 519 0 24 56.0 154.000 23.6 17 72 18 10.50 2 5 4.0 0 0 31.770 1.99 7.700 3.340 #NAME? 30 28 #NAME? 3.850 3.7 27.37 19.700 0.400 92.0 0 0 0 0 0 0 0 110 70 5 4 19.0 20.00 5.90
520 520 0 28 53.0 158.496 21.1 16 72 18 11.80 2 5 5.0 1 0 1.990 1.99 5.570 3.750 #NAME? 28 26 #NAME? 2.080 5.5 23.01 32.000 0.390 100.0 0 0 1 0 0 0 0 110 80 4 4 19.0 16.00 6.50
521 521 1 27 50.0 168.000 17.7 13 72 18 12.00 4 2 7.0 0 0 632.220 1.99 4.560 2.160 #NAME? 26 25 #NAME? 0.860 7.2 23.14 37.900 0.400 100.0 0 1 0 0 1 0 0 120 70 18 20 20.0 19.00 7.80
522 522 0 40 78.0 155.000 32.5 15 74 20 11.20 2 5 14.0 0 0 212.750 1.99 9.140 3.220 #NAME? 48 46 #NAME? 2.090 2.5 25.04 23.400 0.250 92.0 0 1 0 0 1 0 0 120 80 8 10 19.0 21.00 8.00
523 523 0 29 52.0 154.000 21.9 13 72 18 11.80 2 5 8.0 1 0 157.680 1.99 7.080 3.260 #NAME? 28 25 #NAME? 2.780 18.5 16.26 19.000 0.570 140.0 1 0 1 1 0 0 0 110 80 6 5 17.5 16.50 10.00
524 524 1 36 60.0 150.000 26.7 15 72 18 10.20 4 7 8.0 0 0 2.140 1.99 5.710 1.000 #NAME? 42 40 #NAME? 2.110 7.7 39.41 26.100 0.250 110.0 1 0 0 0 1 0 0 110 80 6 9 17.0 22.00 7.60
525 525 1 27 70.0 170.688 24 13 72 18 11.50 2 5 4.0 1 2 1.990 1.99 4.300 2.040 #NAME? 44 42 #NAME? 3.650 0.19 9.83 21.800 0.500 107.0 1 0 0 0 0 0 0 120 80 9 10 21.0 23.00 6.50
526 526 0 27 48.0 158.496 19.1 13 72 18 10.70 2 5 2.0 0 0 1.990 1.99 8.950 4.610 #NAME? 26 24 #NAME? 4.270 0.6 19.46 24.300 0.250 100.0 0 1 0 0 0 0 0 110 70 2 3 16.0 19.00 7.80
527 527 0 32 60.0 155.000 25 16 74 22 10.50 4 7 4.0 1 1 83.950 1.99 3.340 2.610 #NAME? 42 40 #NAME? 1.520 2.5 11.20 12.700 0.250 107.0 0 1 0 0 0 0 0 120 80 5 3 6.8 12.00 6.80
528 528 0 22 62.0 162.000 23.6 15 74 22 11.10 2 4 3.0 0 0 18.750 1.99 4.360 2.660 #NAME? 44 42 #NAME? 2.080 3.7 28.75 21.400 0.250 115.0 0 0 0 0 1 0 0 120 80 10 9 21.0 18.00 6.20
529 529 0 41 58.0 146.304 27.1 13 72 20 10.50 2 6 6.0 0 0 1.990 1.99 9.640 3.680 #NAME? 42 40 #NAME? 2.520 0.8 16.21 31.000 0.250 92.0 1 0 0 0 0 1 0 120 80 1 0 19.0 14.00 9.70
530 530 0 32 52.0 163.000 19.6 13 70 24 10.80 2 6 4.0 0 0 1.990 1.99 1.350 0.370 #NAME? 30 28 #NAME? 5.040 2.3 14.86 19.300 0.250 108.0 0 0 0 0 0 1 0 120 80 3 1 18.0 17.00 7.30
531 531 0 38 61.0 158.000 24.4 15 72 20 12.30 2 5 10.0 0 0 270.490 1.99 9.290 2.540 #NAME? 38 34 #NAME? 1.310 1 19.31 30.500 0.260 100.0 0 0 0 0 0 0 0 120 80 10 11 21.0 12.00 4.80
532 532 0 34 70.0 164.000 26 11 72 22 10.50 2 5 8.0 0 0 1.990 1.99 6.870 2.070 #NAME? 48 44 #NAME? 1.080 0.7 39.32 11.300 0.250 92.0 0 0 0 0 0 0 0 120 70 2 0 21.0 16.00 8.50
533 533 0 39 56.0 158.496 22.3 12 72 22 10.80 2 4 13.0 0 0 667.070 1.99 10.290 5.100 #NAME? 32 28 #NAME? 1.480 6.3 32.14 23.400 0.250 92.0 0 0 0 0 0 0 0 130 80 9 7 17.0 18.00 7.00
534 534 1 26 53.5 161.544 20.5 14 70 18 10.60 4 7 3.0 0 0 572.860 5.81 3.710 4.660 #NAME? 28 26 #NAME? 1.400 19.6 29.57 32.100 0.360 100.0 0 0 0 0 1 0 0 110 80 8 10 18.0 18.00 10.30
535 535 0 24 48.0 158.496 19.1 14 70 18 11.00 2 5 4.0 0 0 615.290 1.99 3.740 9.380 #NAME? 30 28 #NAME? 1.690 18.2 33.87 26.900 0.300 92.0 0 0 0 0 0 0 0 110 80 7 7 20.0 19.00 8.20
536 536 0 26 80.0 161.544 30.7 18 70 18 10.60 2 5 4.0 0 0 1.990 1.99 7.060 3.500 #NAME? 48 44 #NAME? 17.200 7.6 39.01 32.700 0.250 92.0 0 0 0 0 0 0 0 110 80 7 9 13.0 17.50 9.60
537 537 0 35 50.0 164.592 18.5 17 72 16 11.00 2 5 8.0 0 1 1.990 1.99 10.060 1.810 #NAME? 28 26 #NAME? 1.110 1.7 5.30 36.600 0.250 92.0 0 0 0 0 0 0 0 110 70 1 0 17.5 10.00 6.70
538 538 0 30 63.2 158.000 25.3 15 72 18 10.80 2 5 4.0 1 1 80.130 1.99 5.070 2.840 #NAME? 34 32 #NAME? 2.050 5.6 21.09 23.000 0.250 108.0 1 0 0 0 0 0 0 110 70 9 7 19.0 18.00 8.20
539 539 0 36 54.0 152.000 23.4 13 74 20 10.80 2 6 8.0 0 0 1.990 1.99 11.960 2.780 #NAME? 30 28 #NAME? 2.870 3.7 96.41 22.500 0.250 92.0 0 0 0 0 0 0 0 110 80 1 0 18.0 9.00 7.30
540 540 0 27 50.0 150.000 22.2 15 74 20 12.00 4 2 2.0 0 0 292.920 1.99 4.400 4.330 #NAME? 28 26 #NAME? 2.500 5.2 38.89 22.400 0.250 115.0 0 0 0 0 1 0 0 110 70 7 6 18.0 16.00 11.50
541 541 1 23 82.0 165.000 30.1 13 80 20 10.20 4 7 2.0 0 0 1.990 1.99 3.990 4.300 #NAME? 48 46 #NAME? 1.660 20 20.74 17.400 0.370 108.0 1 1 1 1 1 1 0 120 70 9 10 19.0 18.00 6.90

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Data Exploration:

Using the skimr library we can obtain a quick summary statistic of the dataset. The first data set pcos includes 541 observations and a total of 6 variables. The second data set pcos2 includes 541 observations and a total of 45 variables. There seems to be no missing values in both datasets but there is a mixture of character and numeric column types that have to be addressed / calculated. Notice that the column names are not clear enough for readers, this will be tackled in the data preparation section.

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Summary of the pcos data:

Data summary
Name pcos
Number of rows 541
Number of columns 6
_______________________
Column type frequency:
character 1
numeric 5
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
AMH.ng.mL. 0 1 1 5 0 301 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Sl..No 0 1 271.00 156.32 1.00 136.00 271.00 406.00 541.00 ▇▇▇▇▇
Patient.File.No. 0 1 10271.00 156.32 10001.00 10136.00 10271.00 10406.00 10541.00 ▇▇▇▇▇
PCOS..Y.N. 0 1 0.33 0.47 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
I…beta.HCG.mIU.mL. 0 1 664.55 3348.92 1.30 1.99 20.00 297.21 32460.97 ▇▁▁▁▁
II….beta.HCG.mIU.mL. 0 1 238.23 1603.83 0.11 1.99 1.99 97.63 25000.00 ▇▁▁▁▁

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Summary of the pcos2 data:

Data summary
Name pcos2
Number of rows 541
Number of columns 45
_______________________
Column type frequency:
character 6
numeric 39
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
BMI 0 1 2 6 0 108 0
II….beta.HCG.mIU.mL. 0 1 1 8 0 203 0
FSH.LH 0 1 4 6 0 10 0
Waist.Hip.Ratio 0 1 5 6 0 9 0
AMH.ng.mL. 0 1 1 5 0 301 0
X 0 1 0 1 539 3 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Sl..No 0 1 271.00 156.32 1.00 136.00 271.00 406.00 541.00 ▇▇▇▇▇
Patient.File.No. 0 1 271.00 156.32 1.00 136.00 271.00 406.00 541.00 ▇▇▇▇▇
PCOS..Y.N. 0 1 0.33 0.47 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
Age..yrs. 0 1 31.43 5.41 20.00 28.00 31.00 35.00 48.00 ▂▇▆▃▁
Weight..Kg. 0 1 59.64 11.03 31.00 52.00 59.00 65.00 108.00 ▂▇▅▁▁
Height.Cm. 0 1 156.48 6.03 137.00 152.00 156.00 160.00 180.00 ▁▇▇▂▁
Blood.Group 0 1 13.80 1.84 11.00 13.00 14.00 15.00 18.00 ▅▅▇▁▂
Pulse.rate.bpm. 0 1 73.25 4.43 13.00 72.00 72.00 74.00 82.00 ▁▁▁▁▇
RR..breaths.min. 0 1 19.24 1.69 16.00 18.00 18.00 20.00 28.00 ▇▅▂▁▁
Hb.g.dl. 0 1 11.16 0.87 8.50 10.50 11.00 11.70 14.80 ▁▇▅▂▁
Cycle.R.I. 0 1 2.56 0.90 2.00 2.00 2.00 4.00 5.00 ▇▁▁▃▁
Cycle.length.days. 0 1 4.94 1.49 0.00 4.00 5.00 5.00 12.00 ▁▂▇▁▁
Marraige.Status..Yrs. 1 1 7.68 4.80 0.00 4.00 7.00 10.00 30.00 ▇▆▂▁▁
Pregnant.Y.N. 0 1 0.38 0.49 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▅
No..of.aborptions 0 1 0.29 0.69 0.00 0.00 0.00 0.00 5.00 ▇▁▁▁▁
I…beta.HCG.mIU.mL. 0 1 664.55 3348.92 1.30 1.99 20.00 297.21 32460.97 ▇▁▁▁▁
FSH.mIU.mL. 0 1 14.60 217.02 0.21 3.30 4.85 6.41 5052.00 ▇▁▁▁▁
LH.mIU.mL. 0 1 6.47 86.67 0.02 1.02 2.30 3.68 2018.00 ▇▁▁▁▁
Hip.inch. 0 1 37.99 3.97 26.00 36.00 38.00 40.00 48.00 ▁▂▇▃▂
Waist.inch. 0 1 33.84 3.60 24.00 32.00 34.00 36.00 47.00 ▂▇▇▂▁
TSH..mIU.L. 0 1 2.98 3.76 0.04 1.48 2.26 3.57 65.00 ▇▁▁▁▁
PRL.ng.mL. 0 1 24.32 14.97 0.40 14.52 21.92 29.89 128.24 ▇▃▁▁▁
Vit.D3..ng.mL. 0 1 49.92 346.21 0.00 20.80 25.90 34.50 6014.66 ▇▁▁▁▁
PRG.ng.mL. 0 1 0.61 3.81 0.05 0.25 0.32 0.45 85.00 ▇▁▁▁▁
RBS.mg.dl. 0 1 99.84 18.56 60.00 92.00 100.00 107.00 350.00 ▇▁▁▁▁
Weight.gain.Y.N. 0 1 0.38 0.49 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▅
hair.growth.Y.N. 0 1 0.27 0.45 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
Skin.darkening..Y.N. 0 1 0.31 0.46 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
Hair.loss.Y.N. 0 1 0.45 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
Pimples.Y.N. 0 1 0.49 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▇
Fast.food..Y.N. 1 1 0.51 0.50 0.00 0.00 1.00 1.00 1.00 ▇▁▁▁▇
Reg.Exercise.Y.N. 0 1 0.25 0.43 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
BP._Systolic..mmHg. 0 1 114.66 7.38 12.00 110.00 110.00 120.00 140.00 ▁▁▁▇▇
BP._Diastolic..mmHg. 0 1 76.93 5.57 8.00 70.00 80.00 80.00 100.00 ▁▁▁▇▁
Follicle.No…L. 0 1 6.13 4.23 0.00 3.00 5.00 9.00 22.00 ▇▆▃▁▁
Follicle.No…R. 0 1 6.64 4.44 0.00 3.00 6.00 10.00 20.00 ▇▇▅▂▁
Avg..F.size..L…mm. 0 1 15.02 3.57 0.00 13.00 15.00 18.00 24.00 ▁▁▅▇▁
Avg..F.size..R…mm. 0 1 15.45 3.32 0.00 13.00 16.00 18.00 24.00 ▁▁▅▇▁
Endometrium..mm. 0 1 8.48 2.17 0.00 7.00 8.50 9.80 18.00 ▁▅▇▂▁

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Histograms provide valuable visual insights into the distribution patterns within a dataset. With the use of the Data Explorer library it allows us to effortlessly generate histograms that showcase the distribution characteristics of the entire dataset, as illustrated below.

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pcos data:

Upon analyzing the variable distribution, my initial observation indicates that the variables follow a right-skewed distribution. Although the SI..No variable exhibits a unimodal distribution, it lacks significance and will be eliminated upon merging both datasets.

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pcos2 data:

Upon visual inspection, no distinct pattern or discernible shape emerges from the histograms for the second dataset as a whole. Similar to the first dataset, SI..No and Patient.File.No. has a unimodel distribution but plays no significant role in my analysis.

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I would like to further examine these variables but not before preparing the data set to ensure all variables are accounted for especially for BMI, FSH.LH and Waist.Hip.Ratio.

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Data Preparation:

My data preparation primarily involves column renaming and addressing missing values. This includes handling missing values by employing mean, median, or mode replacement strategies for columns like Marraige.Status..Yrs. and Fast.food..Y.N.. Additionally, for columns such as BMI, FSH.LH, and Waist.Hip.Ratio, I will compute overall values. Specific columns will be converted to the metric system, and redundant or unnecessary columns will be removed. These steps aim to streamline data management before merging the two datasets.

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Prior to commencing the cleaning process, I standardized the datasets to numeric format due to variations in the class of certain variables.

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From the skimr summary it was noted that there were no missing values but there was an error in the computational values of certain columns. Below is the initial count of missing values for each data set:

pcos dataset:

##                 Sl..No       Patient.File.No.             PCOS..Y.N. 
##                      0                      0                      0 
##   I...beta.HCG.mIU.mL. II....beta.HCG.mIU.mL.             AMH.ng.mL. 
##                      0                      0                      1

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pcos2 dataset:

##                 Sl..No       Patient.File.No.             PCOS..Y.N. 
##                      0                      0                      0 
##              Age..yrs.            Weight..Kg.             Height.Cm. 
##                      0                      0                      0 
##                    BMI            Blood.Group        Pulse.rate.bpm. 
##                    299                      0                      0 
##       RR..breaths.min.               Hb.g.dl.             Cycle.R.I. 
##                      0                      0                      0 
##     Cycle.length.days.  Marraige.Status..Yrs.          Pregnant.Y.N. 
##                      0                      1                      0 
##      No..of.aborptions   I...beta.HCG.mIU.mL. II....beta.HCG.mIU.mL. 
##                      0                      0                      1 
##            FSH.mIU.mL.             LH.mIU.mL.                 FSH.LH 
##                      0                      0                    532 
##              Hip.inch.            Waist.inch.        Waist.Hip.Ratio 
##                      0                      0                    532 
##            TSH..mIU.L.             AMH.ng.mL.             PRL.ng.mL. 
##                      0                      1                      0 
##         Vit.D3..ng.mL.             PRG.ng.mL.             RBS.mg.dl. 
##                      0                      0                      0 
##       Weight.gain.Y.N.       hair.growth.Y.N.   Skin.darkening..Y.N. 
##                      0                      0                      0 
##         Hair.loss.Y.N.           Pimples.Y.N.        Fast.food..Y.N. 
##                      0                      0                      1 
##      Reg.Exercise.Y.N.    BP._Systolic..mmHg.   BP._Diastolic..mmHg. 
##                      0                      0                      0 
##       Follicle.No...L.       Follicle.No...R.   Avg..F.size..L...mm. 
##                      0                      0                      0 
##   Avg..F.size..R...mm.       Endometrium..mm.                      X 
##                      0                      0                    540

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After making the first part of addressing the column names, the results are seen below of the merged datasets:

Table 3.pcos_data merged data
Sl.No PCOS Age_yrs Weight Height BMI Blood_Group Pulse_rate_bpm RR_breaths_min Hb_gdl Cycle_RI Cycle_length_days Married_yrs Pregnant No_of_abortions IbetaHCG_mIUmL IIbetaHCG_mIUmL FSH_mIUmL LH_mIUmL FSH_LH Hip Waist Waist_Hip_Ratio TSH_mIUmL AMH_ngmL PRL_ngmL Vit_D3_ngmL PRG_ngmL RBS_mgdl Weight_gain Hair_growth Skin_darkening Hair_loss Pimples Fast_food Reg_Exercise BP_Systolic_mmHg BP_Diastolic_mmHg Follicle_NoL Follicle_NoR Avg_F_sizeL_mm Avg_F_sizeR_mm Endometrium_mm
1 0 28 44.6 152.000 19.3 15 78 22 10.48 2 5 7.0 0 0 1.990 1.990 7.950 3.680 NA 36 30 NA 0.680 2.070 45.16 17.100 0.570 92.0 0 0 0 0 0 1 0 110 80 3 3 18.0 18.00 8.50
2 0 36 65.0 161.500 NA 15 74 20 11.70 2 5 11.0 1 0 60.800 1.990 6.730 1.090 NA 38 32 NA 3.160 1.530 20.09 61.300 0.970 92.0 0 0 0 0 0 0 0 120 70 3 5 15.0 14.00 3.70
3 1 33 68.8 165.000 NA 11 72 18 11.80 2 5 10.0 1 0 494.080 494.080 5.540 0.880 NA 40 36 NA 2.540 6.630 10.52 49.700 0.360 84.0 0 0 0 1 1 1 0 120 80 13 15 18.0 20.00 10.00
4 0 37 65.0 148.000 NA 13 72 20 12.00 2 5 4.0 0 0 1.990 1.990 8.060 2.360 NA 42 36 NA 16.410 1.220 36.90 33.400 0.360 76.0 0 0 0 0 0 0 0 120 70 2 2 15.0 14.00 7.50
5 0 25 52.0 161.000 NA 11 72 18 10.00 2 5 1.0 1 0 801.450 801.450 3.980 0.900 NA 37 30 NA 3.570 2.260 30.09 43.800 0.380 84.0 0 0 0 1 0 0 0 120 80 3 4 16.0 14.00 7.00
6 0 36 74.1 165.000 NA 15 78 28 11.20 2 5 8.0 1 0 237.970 1.990 3.240 1.070 NA 44 38 NA 1.600 6.740 16.18 52.400 0.300 76.0 1 0 0 1 0 0 0 110 70 9 6 16.0 20.00 8.00
7 0 34 64.0 156.000 NA 11 72 18 10.90 2 5 2.0 0 0 1.990 1.990 2.850 0.310 NA 39 33 NA 1.510 3.050 26.41 42.700 0.460 93.0 0 0 0 0 0 0 0 120 80 6 6 15.0 16.00 6.80
8 0 33 58.5 159.000 NA 13 72 20 11.00 2 5 13.0 1 2 100.510 100.510 4.860 3.070 NA 44 38 NA 12.180 1.540 3.97 38.000 0.260 91.0 1 0 0 0 0 0 0 120 80 7 6 15.0 18.00 7.10
9 0 32 40.0 158.000 NA 11 72 18 11.80 2 5 8.0 0 1 1.990 1.990 3.760 3.020 NA 39 35 NA 1.510 1.000 19.00 21.800 0.300 116.0 0 0 0 0 0 0 0 120 80 5 7 17.0 17.00 4.20
10 0 36 52.0 150.000 NA 15 80 20 10.00 4 2 4.0 0 0 1.990 1.990 2.800 1.510 NA 40 38 NA 6.650 1.610 11.74 27.700 0.250 125.0 0 0 0 0 0 0 0 110 80 1 1 14.0 17.00 2.50
11 0 20 71.0 163.000 NA 15 80 20 10.00 2 5 4.0 1 2 158.510 158.510 4.890 2.020 NA 39 35 NA 1.560 4.470 13.47 18.100 0.360 108.0 0 0 0 0 0 0 0 110 80 7 15 17.0 20.00 6.00
12 0 26 49.0 160.000 NA 13 72 20 9.50 2 5 3.0 0 1 1.990 1.990 4.090 1.470 NA 39 33 NA 3.980 1.670 21.10 29.180 0.250 100.0 0 0 0 0 0 0 0 120 80 4 2 18.0 19.00 7.80
13 1 25 74.0 152.000 NA 17 72 18 11.70 4 2 7.0 1 0 1214.230 1214.230 2.000 1.510 NA 45 40 NA 6.510 7.940 22.43 31.400 0.300 125.0 1 1 1 1 1 1 1 120 80 15 8 20.0 21.00 8.00
14 0 38 50.0 152.000 NA 13 74 20 12.10 2 5 15.0 0 0 1.990 1.990 4.840 0.710 NA 39 33 NA 1.480 2.380 15.62 21.200 0.400 91.0 0 0 0 0 0 0 0 110 70 3 3 18.0 17.00 5.60
15 0 34 57.3 162.000 NA 13 74 22 11.70 2 5 9.0 0 0 1.990 1.990 7.450 3.710 NA 38 30 NA 1.510 0.880 19.60 24.900 0.260 116.0 0 0 0 1 1 1 0 120 80 4 1 19.0 21.00 5.50
16 0 38 80.5 154.000 NA 13 78 22 11.40 2 5 20.0 0 0 1.990 1.990 9.510 2.510 NA 44 41 NA 1.180 0.690 92.65 9.700 0.300 116.0 1 0 0 0 0 0 0 120 80 1 3 14.0 20.00 3.90
17 0 29 43.0 148.000 NA 13 80 20 11.10 2 5 2.0 1 0 8104.210 91.550 2.020 0.650 NA 36 29 NA 1.980 3.780 20.25 19.100 0.460 91.0 0 0 0 0 0 0 0 110 70 6 5 20.0 20.00 5.60
18 0 36 69.2 160.000 NA 13 72 18 10.80 2 5 7.0 0 0 1.990 1.990 4.860 2.960 NA 39 32 NA 5.000 1.920 12.52 26.000 0.250 100.0 0 0 0 0 0 0 0 110 80 1 2 20.0 18.00 4.00
19 0 31 52.4 159.000 NA 17 72 18 12.70 2 5 7.0 0 0 1.990 1.990 6.050 1.050 NA 37 33 NA 3.190 1.000 12.05 23.300 0.250 127.0 0 0 0 0 0 0 0 120 80 0 2 0.0 17.00 5.60
20 1 30 85.0 165.000 NA 16 72 18 12.50 4 7 7.0 0 0 23.580 1.990 1.890 0.810 NA 44 42 NA 2.870 2.070 19.13 28.000 0.300 100.0 0 1 1 1 1 1 0 120 80 16 8 18.0 17.00 11.00
21 0 25 64.0 156.000 NA 11 70 18 11.20 2 6 6.0 0 0 1.990 1.990 2.820 1.300 NA 39 34 NA 1.860 2.850 33.62 17.100 0.400 91.0 0 0 0 0 0 0 0 110 80 1 2 18.0 19.00 5.70
22 0 38 50.0 156.000 NA 15 72 18 11.00 4 9 12.0 1 0 749.980 749.980 3.180 2.180 NA 36 29 NA 5.710 2.130 40.74 18.900 0.300 84.0 0 0 0 0 0 0 0 120 80 4 2 17.0 17.00 7.60
23 0 34 64.2 155.000 NA 15 74 20 12.10 2 5 10.0 1 0 218.650 218.650 4.080 2.300 NA 36 32 NA 1.250 4.130 13.38 28.600 0.260 116.0 0 0 0 0 0 0 0 110 70 5 7 16.0 17.00 7.60
24 0 28 65.0 152.000 NA 13 74 22 10.50 2 5 5.0 1 0 13.000 13.000 6.410 1.690 NA 37 33 NA 0.450 2.500 17.88 30.540 0.730 92.0 0 0 0 0 1 0 0 110 80 6 8 14.0 18.00 8.50
25 1 34 63.0 158.000 NA 11 72 20 11.20 2 5 12.0 1 0 610.630 610.630 5.340 0.890 NA 38 32 NA 0.650 1.890 11.46 21.500 0.980 100.0 0 1 0 0 1 1 0 120 70 4 6 18.0 17.00 7.30
26 0 41 42.0 152.000 NA 12 80 20 11.50 2 5 15.0 1 0 4490.180 4490.180 8.820 4.390 NA 39 32 NA 0.840 0.260 17.35 45.600 0.980 92.0 0 0 0 0 0 0 0 110 70 1 2 15.0 17.00 8.80
27 1 30 76.0 160.000 NA 15 75 18 11.20 4 3 5.0 0 0 1.990 1.990 6.180 2.780 NA 45 38 NA 4.280 3.840 17.98 25.140 0.350 100.0 1 1 1 1 1 1 1 120 80 21 20 11.0 12.00 6.80
28 0 20 68.0 152.000 NA 17 72 20 10.00 4 3 4.0 1 0 689.580 11.240 1.800 0.410 NA 40 33 NA 8.390 2.500 11.24 46.300 1.200 92.0 0 0 0 0 0 0 0 110 80 3 2 10.0 11.00 10.00
29 0 25 62.0 158.000 NA 15 78 22 11.60 2 5 8.0 0 0 15.000 15.000 4.600 1.060 NA 42 34 NA 3.790 3.560 15.30 36.560 0.250 106.0 0 0 0 0 0 0 0 120 80 7 4 12.0 17.00 6.20
30 1 28 56.0 152.000 NA 13 78 22 11.00 4 3 7.0 0 0 768.030 768.030 2.410 1.190 NA 40 33 NA 3.440 1.560 45.47 23.140 0.630 100.0 1 0 1 0 1 1 0 110 80 11 10 13.0 14.00 5.00
31 0 32 50.0 155.000 NA 17 78 22 10.80 2 4 3.5 1 0 612.320 12.000 6.340 4.070 NA 34 28 NA 1.400 1.690 54.09 56.200 0.310 80.0 0 0 0 0 0 0 0 110 80 10 8 14.0 14.00 7.00
32 1 34 57.0 150.000 NA 11 72 22 12.00 2 4 15.0 0 0 10.000 10.000 5.010 6.480 NA 40 37 NA 4.210 1.890 12.71 32.450 0.270 85.0 1 1 1 1 1 1 0 110 80 5 10 14.0 13.00 6.00
33 0 31 58.0 155.000 NA 15 72 18 10.50 2 5 2.0 0 0 1.990 1.990 2.800 0.420 NA 39 32 NA 1.880 2.340 23.74 32.200 0.520 138.0 0 0 0 0 0 0 0 120 80 8 9 12.0 13.00 7.50
34 0 38 54.0 165.000 NA 15 72 18 10.20 2 5 15.0 0 3 20.000 20.000 6.110 1.970 NA 42 34 NA 2.420 1.580 40.41 36.850 0.620 92.0 0 0 0 0 0 0 0 120 80 2 5 14.0 12.00 8.50
35 0 28 50.0 158.000 NA 15 76 20 10.00 2 5 6.0 1 0 1277.490 30.660 2.170 0.570 NA 32 27 NA 1.660 2.360 55.20 36.870 0.600 85.0 0 0 0 0 0 0 0 120 70 11 7 14.0 19.00 8.00
36 0 32 71.0 165.000 NA 11 72 22 12.00 2 5 10.0 1 0 1455.000 1455.000 2.650 0.520 NA 42 36 NA 2.030 3.640 19.78 25.220 0.450 80.0 0 0 0 0 0 0 0 110 80 1 1 11.0 12.00 7.00
37 0 37 73.0 158.000 NA 15 74 18 10.50 4 7 12.0 0 0 18.000 1.990 3.220 0.480 NA 45 38 NA 3.570 2.780 28.88 36.580 0.270 92.0 0 0 0 0 0 0 0 110 80 6 2 11.0 13.00 10.00
38 0 26 72.0 157.000 NA 11 70 18 11.00 2 5 1.0 0 0 12.000 1.990 6.040 2.320 NA 42 34 NA 65.000 0.880 30.59 20.250 0.880 90.0 0 0 0 0 0 0 0 120 80 1 2 6.5 8.50 10.00
39 0 36 53.0 158.000 NA 13 78 22 11.00 2 5 17.0 0 0 10.000 1.990 3.990 1.990 NA 39 32 NA 2.376 2.360 19.64 45.550 0.650 100.0 0 0 0 0 0 0 0 110 80 7 5 8.0 7.00 15.00
40 0 20 74.0 171.000 NA 13 74 16 11.10 4 0 1.0 0 0 1.990 1.990 3.520 9.170 NA 42 36 NA 2.010 0.330 12.49 38.630 0.320 80.0 0 0 0 0 0 0 0 110 70 6 12 13.0 12.00 0.00
41 0 32 40.0 156.000 NA 15 78 22 10.50 2 5 6.0 0 0 10.000 1.990 7.870 4.890 NA 34 30 NA 5.000 2.350 31.32 56.200 0.250 107.0 0 0 0 0 0 0 0 110 80 5 8 14.0 14.00 7.30
42 0 29 78.0 165.000 NA 13 72 16 12.80 2 5 2.0 1 0 497.410 497.410 5.600 2.130 NA 42 36 NA 0.930 3.880 20.46 14.360 85.000 92.0 0 0 0 0 0 0 0 110 80 8 10 11.0 12.00 6.50
43 0 28 33.0 157.000 NA 13 78 18 11.50 4 9 3.5 0 0 10.000 1.990 3.340 0.250 NA 32 26 NA 3.400 3.550 6.59 42.300 0.250 84.0 0 0 0 0 0 0 0 100 70 0 1 0.0 12.00 6.50
44 0 24 68.0 165.000 NA 15 72 16 11.00 2 5 3.5 1 0 161.147 167.000 2.790 1.970 NA 39 33 NA 1.480 4.330 0.40 25.366 0.250 100.0 0 0 0 0 0 0 0 110 70 6 8 8.0 8.00 7.12
45 1 29 59.0 158.000 NA 17 78 22 12.00 4 7 7.0 1 0 1305.350 1.990 3.260 0.790 NA 39 33 NA 3.770 3.550 31.36 44.550 0.760 100.0 1 1 1 1 1 1 1 120 80 13 14 16.0 15.00 7.00
46 0 25 56.0 155.000 NA 15 70 18 10.60 2 5 6.0 1 0 43.500 9.830 2.770 0.570 NA 40 33 NA 3.850 2.360 25.60 45.500 0.310 92.0 0 0 0 0 0 0 0 120 80 5 8 14.0 13.00 9.40
47 1 28 75.0 165.000 NA 15 72 18 12.00 2 5 2.5 1 0 141.060 141.060 5.810 0.270 NA 45 41 NA 0.830 3.660 14.43 52.300 0.380 100.0 1 1 0 1 1 1 0 110 70 4 6 10.0 12.00 9.00
48 0 26 40.0 156.000 NA 13 74 18 10.20 2 5 3.0 1 0 1500.000 528.500 7.340 2.780 NA 39 33 NA 4.450 4.330 24.58 54.620 0.250 80.0 0 0 0 0 0 0 0 110 70 5 7 15.0 18.00 16.00
49 0 34 51.0 155.000 NA 11 78 24 10.80 2 5 11.0 0 0 10.000 1.990 2.460 0.510 NA 39 34 NA 2.160 1.000 7.97 35.660 0.250 75.0 0 0 0 0 0 1 0 110 80 6 8 10.0 15.00 9.00
50 1 27 51.0 154.000 NA 15 72 18 12.80 4 3 4.0 0 0 10.000 1.990 2.260 2.400 NA 38 34 NA 2.770 4.500 15.22 45.200 0.520 100.0 1 1 1 1 1 1 1 110 70 5 8 14.0 13.00 10.40
51 1 23 68.0 172.000 NA 15 72 16 10.80 4 10 4.0 0 0 77.600 1.990 4.420 5.480 NA 42 34 NA 3.260 3.200 27.65 23.114 0.390 70.0 1 1 1 1 1 0 0 110 70 22 18 12.0 13.00 11.00
52 0 23 54.0 158.000 NA 16 72 18 10.80 2 5 2.0 1 0 177.570 177.570 5.320 1.800 NA 38 32 NA 5.170 2.100 24.50 14.000 0.250 70.0 0 0 0 0 0 0 0 100 70 7 10 15.0 13.00 10.00
53 1 31 50.0 157.000 NA 15 72 20 10.80 2 4 1.5 1 0 160.770 10.000 7.080 4.090 NA 39 32 NA 2.240 4.500 21.38 64.200 0.570 107.0 1 0 1 0 1 1 1 120 80 6 11 12.0 11.00 6.00
54 0 32 62.0 151.000 NA 15 70 18 10.50 2 5 13.0 1 0 180.330 12.000 2.490 0.720 NA 45 39 NA 0.750 6.550 18.51 45.360 0.250 92.0 0 0 0 0 0 0 0 120 80 4 6 13.0 14.00 6.80
55 0 32 58.0 152.000 NA 13 70 18 10.00 2 5 16.0 0 0 1.990 1.990 9.430 6.080 NA 40 36 NA 2.820 1.200 33.78 56.250 0.450 100.0 0 0 0 0 0 0 0 120 80 5 9 10.0 11.00 6.30
56 0 37 68.0 169.000 NA 15 72 18 10.00 2 5 7.0 1 0 274.700 15.000 3.510 0.980 NA 44 37 NA 5.200 2.330 34.42 25.600 0.250 92.0 0 0 0 0 0 0 0 110 80 6 7 9.5 11.00 8.90
57 0 38 56.0 160.000 NA 11 72 20 10.80 2 5 15.0 1 0 1.990 1.990 8.490 2.420 NA 40 34 NA 4.450 3.220 36.45 45.300 0.420 130.0 0 0 0 0 0 0 0 120 80 6 5 11.0 13.00 8.60
58 0 36 58.0 152.000 NA 14 72 20 10.00 2 5 13.0 1 1 249.870 12.000 3.880 0.610 NA 42 35 NA 1.980 2.333 13.47 31.220 0.490 82.0 0 0 0 0 0 0 0 110 80 3 2 12.0 13.00 9.30
59 0 26 75.0 158.000 NA 13 72 18 10.50 2 5 8.0 0 0 1.990 1.990 6.870 3.340 NA 45 40 NA 2.330 2.310 9.35 25.650 0.270 92.0 0 0 0 0 0 0 0 110 80 3 3 16.0 17.00 14.00
60 0 26 67.0 137.000 NA 15 74 20 11.00 4 9 12.0 0 0 1.990 1.990 5.900 2.730 NA 44 39 NA 1.700 2.360 15.19 45.350 0.660 92.0 0 0 0 0 0 0 0 120 70 5 7 14.0 15.00 11.00
61 0 29 63.0 162.000 NA 13 78 22 11.20 2 5 4.0 1 0 626.920 65.030 6.070 3.870 NA 42 36 NA 6.710 4.200 47.72 36.210 0.590 92.0 0 0 0 0 0 0 0 110 80 6 7 13.0 14.00 7.20
62 1 32 76.0 161.000 NA 15 78 22 11.20 2 5 12.0 0 1 8.000 1.990 1.460 0.150 NA 45 39 NA 0.440 3.210 13.16 25.450 0.320 107.0 1 1 1 1 1 1 1 120 80 9 12 14.0 13.00 7.80
63 0 24 60.0 170.000 NA 15 70 20 10.00 2 5 3.5 0 0 1.990 1.990 5.750 7.470 NA 41 36 NA 3.570 2.140 14.73 36.200 0.810 100.0 0 0 0 0 0 0 0 120 80 3 5 12.0 13.50 9.60
64 0 29 55.0 147.000 NA 15 80 20 10.20 4 2 6.0 1 0 757.170 20.000 6.760 9.280 NA 39 34 NA 1.200 2.300 20.79 50.200 0.560 92.0 0 0 0 0 0 0 0 120 70 4 7 11.0 11.00 8.60
65 0 27 56.0 150.000 NA 11 72 18 10.50 4 8 6.0 0 0 1.990 1.990 6.350 7.460 NA 40 35 NA 3.720 4.600 37.92 49.300 3.660 92.0 0 0 0 0 0 0 0 110 80 11 10 14.5 14.00 12.50
66 0 32 71.0 172.000 NA 12 70 18 11.00 2 5 8.0 0 1 10.000 1.990 4.760 1.500 NA 46 39 NA 2.450 2.300 22.45 51.900 0.450 100.0 0 0 0 0 0 0 0 100 70 2 4 12.0 14.00 9.40
67 0 41 56.0 160.000 NA 13 78 20 11.50 2 5 4.0 0 3 31.330 1.990 9.660 2.350 NA 38 34 NA 3.270 5.800 54.32 36.550 0.280 115.0 0 0 0 0 0 0 0 120 80 1 2 10.0 12.00 9.70
68 0 35 62.0 152.000 NA 16 78 22 8.50 2 5 12.0 1 1 218.590 1.990 5.410 8.450 NA 39 33 NA 9.030 5.200 6.27 25.360 0.620 120.0 0 0 0 0 0 0 0 110 80 14 12 8.0 5.00 5.50
69 1 35 55.0 153.000 NA 17 80 20 10.30 2 5 18.0 1 2 218.590 1.990 1.570 0.770 NA 38 34 NA 4.180 2.140 23.45 56.250 0.440 100.0 1 1 1 1 1 0 0 120 70 7 9 5.0 8.50 6.20
70 1 34 65.0 166.000 NA 15 80 20 11.00 2 5 9.0 1 0 173.660 173.660 2.180 0.820 NA 41 36 NA 2.850 4.630 28.90 15.440 0.250 80.0 1 0 1 0 1 0 1 120 70 11 8 9.5 7.00 8.90
71 0 33 54.0 150.000 NA 13 72 18 10.20 2 5 9.0 0 0 4.420 1.990 4.850 0.720 NA 40 33 NA 2.000 1.010 15.64 42.360 0.520 92.0 0 0 0 0 0 0 0 100 70 5 2 11.5 4.70 6.50
72 0 29 61.0 151.000 NA 13 74 20 10.00 2 5 9.0 0 0 15.000 1.990 6.490 0.990 NA 42 35 NA 1.660 2.580 41.63 38.440 1.220 92.0 0 0 0 0 0 0 0 120 70 5 6 12.0 6.00 6.00
73 0 36 65.0 154.000 NA 15 70 18 10.00 2 5 3.0 1 0 161.490 161.490 4.950 5.350 NA 43 38 NA 3.170 5.800 32.07 56.450 0.790 92.0 0 0 0 0 0 0 0 110 80 3 1 7.5 4.50 5.40
74 0 26 70.0 163.000 NA 15 72 18 10.50 2 5 3.0 0 2 1.990 1.990 7.310 6.680 NA 45 40 NA 2.950 0.350 22.88 14.360 0.270 92.0 1 0 0 0 0 0 0 110 80 3 4 10.5 6.00 9.80
75 0 35 59.0 152.000 NA 13 72 20 11.10 4 12 10.0 1 2 603.810 15.000 5.500 4.260 NA 42 36 NA 3.950 5.230 31.96 37.650 0.250 93.0 0 0 0 0 0 0 0 120 80 2 3 9.5 6.20 8.70
76 0 36 72.0 154.000 NA 13 72 18 10.20 2 4 10.0 1 0 486.390 3.980 6.720 2.830 NA 44 37 NA 2.240 3.680 19.80 10.700 0.350 100.0 0 0 0 0 0 0 0 110 80 0 2 0.0 5.80 8.50
77 1 32 43.0 152.000 NA 13 80 20 10.50 4 11 6.0 1 0 756.610 756.610 4.960 2.060 NA 40 34 NA 4.360 2.140 25.48 38.250 0.350 92.0 0 1 1 1 0 1 0 120 70 4 4 10.5 10.50 12.80
78 0 34 52.0 150.000 NA 11 72 18 12.00 2 5 0.0 0 0 1.990 1.990 5.300 2.150 NA 41 36 NA 4.600 2.550 16.09 35.980 0.150 80.0 0 0 0 0 0 0 0 110 70 7 4 15.0 12.00 18.00
79 0 37 48.0 150.000 NA 11 75 20 10.20 2 5 2.0 0 1 1.990 1.990 15.450 4.364 NA 38 34 NA 1.200 4.910 17.05 29.470 0.520 98.0 0 0 0 0 0 0 0 110 70 0 2 0.0 9.00 8.60
80 0 38 108.0 168.000 NA 14 80 20 11.00 2 5 12.0 0 3 10.000 1.990 5.010 2.600 NA 48 41 NA 1.870 1.030 27.33 36.850 0.540 100.0 0 0 0 0 0 0 0 110 70 1 2 8.0 9.00 10.00
81 0 36 57.6 151.000 NA 11 72 18 11.80 4 2 10.0 0 0 1.990 1.990 6.240 3.240 NA 39 34 NA 2.020 6.560 4.25 27.800 0.300 100.0 0 0 0 0 0 0 0 120 70 8 6 13.0 15.00 8.50
82 0 34 60.0 162.000 NA 13 72 16 12.50 2 5 7.0 1 0 255.020 255.020 3.390 0.410 NA 40 36 NA 1.490 3.910 21.14 29.800 0.310 133.0 0 0 0 0 0 0 0 120 80 8 6 16.0 15.00 8.30
83 0 39 52.0 161.000 NA 11 78 20 10.80 2 5 9.0 0 0 1.990 1.990 4.840 7.580 NA 39 35 NA 3.630 5.420 8.18 21.300 0.360 91.0 0 0 0 0 0 0 0 110 80 2 2 15.0 17.00 0.00
84 0 35 43.7 146.000 NA 11 72 20 10.40 2 5 8.0 0 0 1.990 1.990 6.360 1.940 NA 40 34 NA 0.600 1.650 20.26 38.200 0.260 108.0 0 0 0 0 0 0 0 120 80 2 6 14.0 18.00 6.00
85 0 34 60.0 156.000 NA 14 74 20 12.50 2 5 7.0 0 0 1.990 1.990 6.540 2.190 NA 39 32 NA 4.240 2.060 22.13 37.100 0.260 133.0 0 0 0 0 0 0 0 120 70 2 2 13.0 15.00 10.00
86 0 27 61.6 153.000 NA 15 70 18 11.20 2 5 7.0 1 0 273.700 273.700 3.590 1.070 NA 41 36 NA 1.470 1.810 20.37 28.500 0.300 91.0 0 0 0 0 0 0 0 120 80 4 4 16.0 13.00 12.00
87 0 31 64.0 156.000 NA 15 74 18 12.10 4 9 5.0 0 0 1.990 1.990 2.340 0.390 NA 42 36 NA 2.700 3.810 22.52 29.800 0.300 84.0 0 0 0 0 0 0 0 120 80 8 10 15.0 13.00 10.00
88 0 40 56.0 156.000 NA 11 80 20 10.30 2 5 8.0 1 0 162.050 1.990 1.000 0.020 NA 38 34 NA 1.520 2.260 28.08 55.900 0.750 125.0 0 0 0 0 0 0 0 120 70 11 9 17.0 18.00 4.60
89 1 22 69.5 168.000 NA 13 74 20 12.50 4 2 3.0 0 0 1.990 1.990 5.710 1.440 NA 44 39 NA 1.310 3.650 44.89 27.700 0.250 100.0 0 0 0 0 0 0 0 110 80 10 13 15.0 16.00 10.00
90 0 36 65.0 151.000 NA 15 72 18 10.20 2 5 11.0 0 0 1.990 1.990 4.240 1.200 NA 41 36 NA 2.800 8.980 19.30 11.300 0.560 134.0 0 0 0 0 0 0 0 120 70 11 9 15.0 16.00 8.00
91 0 44 74.4 158.000 NA 17 78 22 11.10 2 5 22.0 0 2 1.990 1.990 4.210 3.650 NA 48 42 NA 3.990 1.700 7.72 34.700 0.300 109.0 0 0 0 0 0 0 0 110 80 4 2 15.0 7.00 6.00
92 0 35 45.0 150.000 NA 15 74 20 11.80 2 5 6.0 0 0 1.990 1.990 3.250 1.560 NA 42 36 NA 1.750 3.180 15.79 34.200 0.310 116.0 0 0 0 0 0 0 0 120 80 7 5 20.0 18.00 5.70
93 0 28 83.5 163.000 NA 15 78 22 12.50 4 11 8.0 0 0 1.990 1.990 4.360 2.690 NA 38 33 NA 2.630 2.750 13.78 29.410 0.350 116.0 0 0 0 0 0 0 0 110 80 2 2 14.0 15.00 8.30
94 0 32 52.0 156.000 NA 11 72 20 10.80 2 5 3.0 0 0 1.990 1.990 5.100 8.960 NA 38 32 NA 4.300 0.860 20.01 29.800 0.370 97.0 0 0 0 0 0 0 0 120 80 2 1 14.0 15.00 9.00
95 0 27 62.5 154.000 NA 14 70 20 12.40 2 5 5.0 0 0 1.990 1.990 5.320 2.790 NA 41 34 NA 6.810 2.290 27.82 22.500 0.360 116.0 0 0 0 0 0 0 0 120 80 3 5 20.0 19.00 6.80
96 1 22 50.0 155.000 NA 15 80 20 12.10 2 5 1.0 1 0 1247.560 15.000 2.000 1.000 NA 38 32 NA 2.780 2.190 27.86 18.700 0.300 107.0 1 1 1 1 1 1 0 120 70 6 10 19.0 19.00 6.00
97 1 28 67.5 154.000 NA 16 78 24 11.70 2 5 2.0 0 3 15.000 1.990 4.660 2.430 NA 41 36 NA 4.640 8.460 28.93 18.700 0.400 104.0 0 0 0 0 0 0 0 110 70 18 13 17.0 18.00 5.90
98 1 31 91.4 154.000 NA 15 78 24 10.70 4 12 10.0 1 0 320.560 14.460 3.680 2.080 NA 39 32 NA 5.000 4.590 40.04 28.300 0.410 108.0 1 1 1 1 1 1 0 110 70 13 10 19.0 16.00 5.00
99 0 32 61.0 163.000 NA 11 72 20 11.00 2 5 13.0 1 0 145.890 145.890 5.280 3.950 NA 40 39 NA 4.360 1.040 30.98 19.200 0.360 125.0 0 0 0 0 0 0 0 120 70 3 2 20.0 18.00 5.00
100 0 32 61.7 156.000 NA 15 78 22 12.20 2 5 8.0 0 5 5.030 1.990 5.640 0.790 NA 41 34 NA 4.140 4.270 13.66 37.200 0.400 60.0 0 0 0 0 0 0 0 110 80 4 2 18.0 18.00 7.50
101 0 30 62.2 158.000 NA 13 72 18 10.80 2 5 5.0 1 1 100.090 100.090 1.480 0.300 NA 42 37 NA 1.440 3.860 20.08 16.900 0.980 116.0 0 0 0 0 0 0 0 120 80 7 10 19.0 20.00 6.80
102 0 34 74.1 162.000 NA 11 78 22 12.40 4 11 13.0 0 0 1.990 1.990 1.470 0.300 NA 41 34 NA 1.560 1.420 13.18 44.500 0.300 131.0 0 0 0 0 0 0 0 110 80 5 2 17.5 18.00 6.40
103 1 38 64.3 149.000 NA 11 78 20 12.90 4 2 16.0 1 1 110.170 110.170 10.520 2.530 NA 42 36 NA 1.320 10.070 14.52 70.530 0.100 83.2 1 1 1 1 1 1 0 110 70 12 12 17.0 9.00 11.30
104 0 34 57.0 160.000 NA 15 72 20 11.80 2 5 12.0 1 1 174.370 174.370 9.400 3.720 NA 38 32 NA 4.050 0.980 17.55 24.300 0.380 92.0 0 0 0 0 0 0 0 120 80 1 0 14.0 0.00 7.00
105 0 29 60.0 159.000 NA 13 72 18 11.20 4 9 6.0 1 0 5202.590 75.510 7.110 7.450 NA 39 32 NA 5.000 4.070 10.12 23.600 0.360 91.0 0 0 0 0 0 0 0 120 80 11 12 18.0 20.00 7.00
106 1 25 68.6 180.000 NA 11 74 20 10.00 2 5 4.0 1 0 600.180 1.990 4.960 4.750 NA 44 38 NA 2.000 3.200 30.74 20.700 0.430 115.0 1 0 1 0 1 0 1 110 70 12 8 18.0 17.50 9.00
107 0 28 60.0 160.000 NA 15 72 20 10.80 4 7 3.0 0 0 1.990 1.990 2.900 0.220 NA 38 32 NA 1.780 3.900 6.54 30.500 0.250 92.0 1 1 1 1 1 0 0 120 80 6 4 19.0 16.00 4.40
108 1 27 54.0 156.000 NA 13 72 18 11.80 4 8 2.0 0 0 120.960 1.990 2.620 0.330 NA 44 37 NA 1.580 10.000 26.29 34.100 0.250 100.0 1 1 0 1 1 1 0 110 80 11 10 16.0 14.00 7.00
109 1 24 53.0 149.000 NA 15 72 22 13.20 4 9 3.0 1 0 397.080 3893.060 4.620 6.030 NA 45 39 NA 1.460 16.900 9.82 15.300 0.980 120.0 1 1 1 0 0 1 1 110 70 11 10 15.0 16.00 9.00
110 0 34 80.0 160.000 NA 15 74 20 10.00 4 11 19.0 1 0 926.240 600.230 4.860 1.970 NA 42 37 NA 1.040 17.000 19.45 39.400 0.340 108.0 0 1 1 0 0 1 1 120 70 5 4 17.0 21.00 9.30
111 0 21 59.0 150.000 NA 15 72 18 11.20 4 6 2.0 1 0 26290.260 3350.190 4.130 5.110 NA 41 34 NA 1.260 21.900 13.97 33.400 1.000 125.0 0 0 1 1 0 0 1 100 70 1 1 11.0 10.00 8.50
112 0 26 75.0 170.000 NA 15 72 18 11.20 4 2 3.0 1 0 32460.970 97.630 6.210 2.180 NA 40 34 NA 2.440 1.600 21.48 25.300 0.280 100.0 0 1 1 1 0 1 1 120 80 5 4 17.0 19.00 10.00
113 0 39 71.2 170.000 NA 15 72 20 10.10 2 5 12.0 0 0 14.470 14.400 4.610 3.370 NA 41 36 NA 4.330 3.300 13.92 29.000 0.660 100.0 1 0 0 1 0 0 0 120 80 5 3 19.0 20.00 7.00
114 1 32 63.0 164.000 NA 17 72 18 11.00 4 9 9.0 0 0 332.510 2.000 5.020 7.520 NA 46 40 NA 1.460 21.000 46.05 32.900 0.680 92.0 1 0 1 1 1 1 1 110 80 5 6 18.0 21.00 9.00
115 1 29 74.0 162.000 NA 15 72 18 11.30 2 5 6.0 0 0 70.170 2.000 4.520 1.920 NA 45 38 NA 1.680 12.700 11.03 22.700 0.700 100.0 1 1 1 1 0 0 1 110 80 14 5 15.0 14.00 8.70
116 0 29 67.0 158.000 NA 11 72 18 11.20 2 5 2.0 0 0 177.570 177.580 5.360 0.420 NA 38 32 NA 0.690 1.800 9.09 15.300 0.630 108.0 1 0 1 0 0 0 1 110 70 5 3 15.5 15.00 8.30
117 0 35 63.0 155.000 NA 15 80 22 10.70 2 5 14.0 0 0 3.100 12.170 9.810 0.340 NA 36 30 NA 2.280 3.600 29.97 24.300 0.930 100.0 0 1 0 0 1 1 1 110 70 4 4 15.0 14.00 9.80
118 1 28 68.0 155.000 NA 13 74 20 11.20 4 9 7.0 0 0 59.390 2.000 5.710 4.310 NA 45 37 NA 2.410 15.000 21.86 18.300 0.350 100.0 1 0 1 1 1 1 1 110 80 21 20 15.0 17.00 11.00
119 0 33 83.0 162.000 NA 13 78 22 11.00 2 5 10.0 0 0 100.680 25.300 4.740 1.490 NA 38 31 NA 0.570 5.000 14.72 32.400 0.260 100.0 0 0 1 1 0 1 0 110 80 7 9 18.0 20.00 8.00
120 0 29 55.0 152.000 NA 11 72 18 11.20 2 5 3.0 0 0 566.240 100.200 3.620 4.250 NA 39 32 NA 1.670 3.300 12.56 17.500 0.480 100.0 1 0 0 0 0 0 0 110 80 2 4 18.0 20.00 11.70
121 0 30 63.0 159.000 NA 15 76 20 10.50 4 9 5.0 0 0 1.990 479.660 7.780 14.690 NA 40 33 NA 2.900 3.300 24.15 56.300 0.440 100.0 0 0 0 0 0 1 1 110 70 3 2 16.0 14.00 8.60
122 0 33 48.0 148.000 NA 16 70 18 10.00 2 5 12.0 0 0 357.090 1.900 7.600 0.480 NA 38 30 NA 1.670 3.900 27.45 33.400 6.390 100.0 0 1 0 0 0 0 0 110 80 1 1 15.0 18.00 6.20
123 1 22 79.0 155.000 NA 11 72 18 11.30 2 5 4.0 0 0 138.680 586.060 4.140 4.060 NA 45 37 NA 2.900 17.900 21.92 22.920 0.420 92.0 1 0 1 0 1 1 1 110 80 6 10 16.0 15.00 13.40
124 1 23 40.0 150.000 NA 15 74 18 10.80 2 5 2.0 0 0 1.990 NA 5.000 5.150 NA 46 38 NA 1.690 19.800 15.23 23.500 0.930 92.0 1 1 1 1 0 1 1 100 70 10 13 18.0 17.00 12.50
125 1 26 78.0 159.000 NA 13 78 20 11.00 2 5 4.0 0 0 1.990 1.990 4.880 5.840 NA 45 39 NA 2.310 9.200 31.53 56.200 0.400 100.0 1 0 1 1 1 1 1 120 80 8 10 16.0 18.00 8.50
126 0 38 54.0 155.000 NA 13 72 22 10.80 2 5 4.0 0 1 363.130 2.800 8.360 0.780 NA 41 33 NA 2.430 2.400 33.51 13.500 0.500 92.0 1 0 0 0 0 0 0 120 80 7 6 18.0 17.00 12.00
127 0 40 56.0 154.000 NA 15 72 18 10.20 2 5 14.0 0 0 84.790 1.990 5.010 1.940 NA 42 33 NA 2.080 4.500 26.34 52.200 0.910 98.0 0 0 0 1 0 0 1 110 80 2 3 18.0 18.00 8.50
128 0 42 48.0 148.000 NA 11 72 18 11.20 2 5 8.0 0 0 1.990 15.360 6.810 3.830 NA 40 32 NA 1.990 5.140 18.02 9.200 0.320 100.0 0 0 0 0 0 0 0 110 70 1 0 17.0 15.00 9.00
129 0 41 54.0 150.000 NA 15 70 18 11.50 2 5 10.0 0 0 98.910 54.080 7.370 1.510 NA 38 30 NA 15.680 2.400 20.16 25.600 0.280 92.0 0 0 0 0 0 0 1 110 80 0 1 15.0 9.00 14.00
130 0 40 40.0 145.000 NA 11 72 18 10.20 2 5 4.0 0 4 1.990 1.990 4.960 0.690 NA 38 31 NA 1.860 0.300 45.56 36.100 0.350 92.0 0 1 0 0 0 0 0 110 80 2 1 19.0 12.00 9.70
131 1 24 89.0 173.000 NA 17 72 18 11.20 4 2 4.0 0 0 152.130 152.130 5.900 9.900 NA 44 38 NA 1.730 11.480 42.43 13.000 0.600 91.0 1 0 1 0 1 1 1 120 80 4 8 16.0 18.00 8.00
132 0 25 53.4 159.000 NA 11 72 18 10.50 2 5 4.0 0 0 1.990 1.990 6.540 10.750 NA 36 30 NA 1.290 19.300 17.68 36.800 0.450 100.0 0 0 0 0 1 1 0 110 70 3 7 15.0 14.00 11.50
133 1 22 60.0 154.000 NA 15 70 18 12.70 2 5 4.0 0 0 1.990 1.990 6.440 5.780 NA 45 34 NA 2.960 8.800 26.72 41.700 0.300 85.0 1 1 1 0 0 1 0 110 80 3 3 16.0 17.00 9.70
134 1 28 70.0 157.000 NA 15 70 18 11.00 2 5 5.0 0 1 342.160 1.990 3.410 3.580 NA 42 36 NA 1.130 19.000 21.70 18.800 0.560 120.0 1 1 1 0 0 1 1 110 80 12 9 19.0 18.00 12.00
135 0 35 68.0 154.000 NA 15 72 18 12.00 2 5 10.0 0 0 3.050 3.050 4.830 1.150 NA 38 30 NA 2.260 4.300 14.79 19.200 0.410 85.0 0 0 0 0 1 0 0 120 80 3 4 18.0 17.00 9.00
136 0 30 71.0 160.000 NA 15 72 18 12.00 2 5 10.0 0 2 1.990 1.990 6.020 3.400 NA 39 31 NA 1.330 1.400 13.87 26.870 0.720 100.0 0 0 1 0 0 0 0 120 80 4 7 15.0 17.00 11.00
137 0 26 60.0 153.000 NA 13 78 22 11.50 2 5 4.0 0 0 201.360 201.360 3.590 2.540 NA 37 30 NA 0.250 12.600 8.12 26.600 0.580 123.0 1 0 0 1 0 0 1 110 80 4 4 14.0 13.00 11.00
138 0 35 40.0 144.000 NA 17 72 18 10.80 2 5 4.0 0 0 1.990 1.990 3.080 1.280 NA 39 33 NA 1.510 4.800 55.47 30.900 0.530 100.0 0 1 0 0 0 0 0 100 70 6 4 17.0 18.00 10.70
139 0 27 59.0 160.000 NA 15 70 18 10.80 2 5 5.0 1 0 21977.290 16069.690 3.140 0.090 NA 40 34 NA 0.040 4.600 19.99 47.000 0.530 84.0 0 0 0 1 1 0 0 110 70 8 5 18.0 15.00 9.00
140 0 35 65.0 154.000 NA 15 72 18 10.70 2 5 5.0 0 0 1.990 1.990 2.360 1.160 NA 38 30 NA 3.850 17.100 6.60 25.800 0.440 120.0 0 0 0 0 0 0 0 120 70 5 7 15.0 13.00 8.50
141 0 36 51.0 150.000 NA 13 72 18 10.20 2 5 8.0 0 0 3.410 57.080 7.920 1.030 NA 37 30 NA 4.040 2.100 27.85 26.500 0.500 92.0 1 0 1 0 0 0 1 110 80 3 2 9.0 18.00 9.00
142 1 28 57.0 158.000 NA 15 74 22 10.80 2 5 5.0 0 0 1.990 1.990 2.810 3.270 NA 42 33 NA 5.000 11.600 16.98 19.900 0.250 92.0 1 0 1 0 1 1 1 110 70 13 12 16.0 18.00 11.00
143 1 30 69.0 158.000 NA 13 72 18 10.40 4 11 14.0 0 0 1.990 1.990 6.400 3.270 NA 42 32 NA 4.180 18.400 23.17 41.800 0.460 100.0 1 1 0 1 1 1 1 110 70 6 7 18.5 16.00 8.00
144 0 32 45.0 150.000 NA 15 70 18 10.80 2 5 7.0 0 0 1.990 1.990 11.120 4.220 NA 38 31 NA 0.900 1.800 3.81 27.900 0.250 110.0 0 0 0 0 0 1 1 110 80 0 5 0.0 19.00 7.00
145 0 37 68.0 153.000 NA 15 80 22 11.20 2 5 14.0 0 0 1.990 1.990 4.190 1.490 NA 37 30 NA 1.400 9.900 10.01 23.500 0.580 130.0 0 0 0 1 0 1 1 120 80 3 3 21.0 18.00 9.30
146 0 35 68.0 160.000 NA 15 74 18 11.10 2 5 4.0 0 0 1.990 1.990 7.420 2.100 NA 39 32 NA 1.620 3.700 18.60 20.100 0.700 108.0 0 0 0 0 0 0 0 120 70 9 10 17.0 18.00 7.50
147 0 36 85.0 164.000 NA 13 72 18 10.20 2 5 12.0 0 1 1.990 1.990 6.280 1.280 NA 40 33 NA 3.260 2.900 12.28 10.100 0.320 160.0 0 0 0 1 0 0 0 120 70 0 4 0.0 18.00 10.70
148 0 28 72.0 160.000 NA 17 70 18 10.00 2 5 3.0 0 0 1.990 1.990 8.960 0.750 NA 38 32 NA 1.040 2.000 24.51 20.000 0.600 92.0 0 1 1 1 0 0 0 110 80 3 3 17.0 18.00 8.00
149 1 23 53.0 152.000 NA 11 72 18 11.00 4 2 3.5 0 0 1.990 1.990 6.230 3.250 NA 45 40 NA 0.050 4.000 28.17 24.900 0.380 92.0 1 1 0 1 0 1 1 110 80 4 12 18.0 16.00 10.00
150 0 24 52.0 152.000 NA 11 80 20 11.00 2 5 4.0 0 0 1.990 1.990 5.450 3.010 NA 39 32 NA 3.460 1.600 77.32 19.500 0.380 92.0 0 0 0 1 0 0 0 110 70 6 2 19.0 19.00 7.10
151 1 28 63.0 162.000 NA 15 78 18 10.80 4 2 5.0 0 0 232.710 232.710 4.210 9.500 NA 44 38 NA 0.650 15.900 17.28 23.800 0.980 110.0 1 1 0 1 0 1 1 110 80 4 9 15.0 18.00 10.00
152 1 29 56.4 150.000 25.1 13 76 22 10.50 4 4 6.0 0 0 1.990 1.990 2.910 0.400 7.250 44 35 0.795 1.640 7.510 10.60 31.200 0.390 108.0 1 1 0 1 0 1 0 110 80 12 14 14.0 18.00 8.00
153 1 31 60.0 156.000 24.7 13 72 18 11.90 4 5 10.0 0 1 1.990 1.990 2.680 0.650 4.120 46 36 0.782 1.000 10.040 13.01 41.890 0.240 100.0 1 1 1 1 1 1 0 110 80 7 5 15.0 16.00 8.50
154 1 29 52.0 147.000 24.1 11 74 18 11.50 4 4 8.0 0 0 1.990 1.990 6.650 12.950 0.513 40 35 0.875 1.890 6.860 27.58 21.220 0.440 116.0 1 0 0 1 0 0 0 110 80 3 5 16.0 19.00 8.00
155 0 26 40.0 152.400 17.2 11 72 20 10.80 2 5 3.0 1 0 60.800 23.500 6.560 5.220 1.250 38 32 0.842 1.660 7.020 23.69 14.500 0.250 100.0 0 0 0 1 0 0 0 120 80 6 5 12.0 14.00 14.00
156 0 26 49.3 158.000 19.7 15 76 18 12.50 4 4 4.0 0 0 1.990 1.990 5.090 3.130 1.626 45 35 0.777 2.560 8.750 44.27 9.600 0.250 73.0 0 1 0 0 0 0 0 120 80 7 7 14.0 16.00 7.50
157 0 27 53.2 158.000 21.3 13 72 22 10.50 4 4 5.0 0 0 1.990 1.990 3.090 0.590 5.237 37 32 0.864 2.030 5.270 7.16 25.300 0.360 91.0 0 0 1 1 0 NA 1 120 70 5 7 11.0 13.00 11.00
158 0 34 64.0 158.000 NA 15 73 20 11.80 2 6 12.0 1 1 289.360 180.300 6.980 1.830 NA 42 34 NA 2.960 1.400 24.89 56.900 0.400 110.0 1 0 1 1 1 1 0 120 80 3 5 12.0 12.00 10.00
159 1 25 60.0 151.000 NA 15 74 18 11.70 2 4 3.0 0 0 1.990 1.990 6.960 5.110 NA 40 34 NA 3.060 9.000 25.04 33.600 0.250 100.0 1 1 0 1 1 1 0 110 70 9 7 13.0 16.00 9.00
160 0 32 64.0 158.000 NA 15 73 18 11.20 2 5 10.0 1 2 366.800 102.300 4.330 2.480 NA 38 32 NA 1.510 3.560 8.21 31.500 0.210 91.0 0 0 1 1 0 0 0 110 80 5 7 11.0 12.00 9.50
161 0 36 50.0 143.000 NA 13 72 20 10.50 2 6 15.0 1 1 96.200 1.990 1.630 0.250 NA 36 32 NA 0.810 3.410 18.06 18.500 0.450 116.0 0 0 0 1 1 0 0 120 80 3 5 11.0 13.00 6.00
162 0 38 70.0 170.000 NA 13 75 20 12.50 2 5 18.0 0 3 1.990 1.990 2.500 2.530 NA 40 34 NA 1.520 0.450 13.56 24.000 0.320 92.0 1 1 1 0 0 0 0 12 80 1 1 11.0 12.00 5.00
163 1 23 65.0 158.000 NA 15 73 20 11.20 2 4 3.0 0 0 1.990 1.990 4.420 5.500 NA 42 35 NA 1.790 2.530 26.64 54.600 0.250 100.0 1 0 1 0 0 1 1 120 70 10 11 14.0 16.00 9.40
164 0 32 60.8 156.000 NA 13 72 18 11.50 2 5 10.0 0 0 1.990 1.990 5.010 2.620 NA 38 32 NA 1.930 0.290 17.70 9.400 0.250 92.0 1 0 0 1 0 1 1 120 80 3 2 10.0 11.00 8.50
165 0 35 56.0 145.000 NA 17 74 18 11.50 4 5 15.0 1 1 481.300 481.300 7.720 3.350 NA 40 33 NA 5.510 2.600 38.93 21.500 1.260 108.0 1 0 1 0 1 0 1 120 80 2 3 13.0 14.00 4.60
166 0 32 46.0 150.000 NA 11 73 26 11.90 2 6 10.0 0 0 1.990 1.990 3.990 0.510 NA 36 30 NA 2.040 2.830 28.71 60.200 0.150 84.0 0 0 0 1 1 1 0 110 60 5 5 14.0 13.00 6.50
167 0 29 62.0 154.000 NA 11 74 18 13.40 2 5 6.0 1 1 563.800 563.800 6.110 4.190 NA 38 32 NA 3.270 1.890 26.00 15.000 0.380 91.0 1 0 0 0 1 1 0 120 80 4 6 12.0 12.00 4.00
168 1 29 60.0 150.000 NA 15 73 20 10.70 2 6 10.0 0 0 2.590 1.990 10.206 2.010 NA 38 30 NA 10.890 2.010 8.45 13.600 0.410 106.0 1 1 0 1 1 1 0 120 80 11 12 14.0 15.00 10.00
169 1 30 72.3 158.000 NA 15 74 20 10.80 4 4 8.0 0 0 1.990 1.990 1.000 0.980 NA 40 34 NA 16.990 2.830 15.77 22.800 0.250 95.0 1 1 1 1 1 1 0 120 80 9 12 14.0 16.00 9.00
170 1 33 59.6 163.000 NA 11 72 20 11.50 4 5 10.0 0 1 4.360 4.320 3.840 1.220 NA 40 32 NA 2.480 5.670 6.06 54.700 0.250 100.0 1 1 0 1 1 1 1 120 80 18 20 16.0 15.00 10.70
171 0 28 54.0 157.000 NA 15 73 19 11.00 2 6 6.0 1 0 833.500 230.500 4.730 3.230 NA 36 34 NA 1.220 1.680 23.54 33.550 0.250 115.0 0 0 0 1 1 0 0 120 80 12 14 13.0 15.00 9.60
172 1 29 60.0 156.000 NA 13 74 18 11.20 4 4 8.0 0 0 1.990 1.990 5.430 6.820 NA 42 36 NA 3.550 3.650 16.41 26.600 0.120 91.0 1 1 0 1 1 1 0 120 80 13 12 15.0 16.00 8.50
173 0 31 50.0 149.000 NA 13 75 18 10.40 2 6 12.0 1 1 2036.200 155.300 1.920 0.340 NA 36 30 NA 2.730 3.630 14.98 48.000 0.900 91.0 0 0 1 0 1 0 1 120 80 3 2 14.0 12.00 5.50
174 1 35 73.5 155.000 NA 15 72 20 10.09 4 5 10.0 0 2 3.888 3.888 3.130 0.440 NA 42 34 NA 0.760 3.490 16.65 31.200 0.410 108.0 1 1 1 1 0 1 0 120 80 15 16 13.0 14.00 9.50
175 0 37 60.0 152.000 NA 11 72 18 11.70 2 6 15.0 1 1 698.200 523.600 9.100 5.100 NA 38 32 NA 2.850 2.010 10.78 17.100 1.000 91.0 1 0 1 0 0 1 0 120 80 1 1 10.0 11.00 5.00
176 1 27 58.0 152.000 NA 15 74 18 11.10 4 4 5.0 0 0 1.990 1.990 6.250 7.350 NA 36 30 NA 2.610 8.000 30.00 6.500 0.251 84.0 1 0 1 1 1 1 0 120 80 12 13 15.0 16.00 9.00
177 1 28 78.0 158.000 NA 16 73 18 10.50 2 4 8.0 0 0 3.660 1.650 4.500 2.770 NA 42 38 NA 4.040 9.000 20.76 10.220 0.260 108.0 1 1 1 1 0 1 0 110 70 15 14 14.0 12.00 8.00
178 1 24 89.0 173.000 NA 17 74 18 11.20 4 4 2.5 0 0 1.990 1.990 5.900 9.900 NA 40 35 NA 1.730 11.480 42.43 25.690 0.350 91.0 1 1 0 1 1 1 0 120 80 11 8 15.0 14.00 8.00
179 1 37 88.0 168.000 NA 12 74 18 11.90 2 5 15.0 0 1 3.830 3.830 9.220 3.220 NA 41 38 NA 1.680 10.250 3.64 48.960 0.580 350.0 1 1 1 1 0 1 0 110 80 12 14 14.0 16.00 11.00
180 0 35 58.0 164.500 NA 11 72 20 12.00 2 6 13.0 1 0 2045.300 569.100 6.270 5.280 NA 36 32 NA 6.950 2.360 23.98 27.200 0.250 100.0 0 0 0 1 1 0 0 140 70 3 5 10.0 11.00 5.00
181 1 30 70.0 150.000 NA 16 74 18 11.70 4 4 8.0 0 0 1.990 1.990 1.980 0.032 NA 38 32 NA 1.980 32.000 14.13 74.500 0.047 100.0 1 1 0 1 0 1 0 120 80 8 6 12.0 11.00 4.50
182 0 30 59.0 167.700 NA 13 73 20 13.80 2 5 8.0 0 0 1.300 1.990 2.050 1.020 NA 38 34 NA 1.840 3.380 35.68 39.810 0.380 116.0 0 1 1 0 0 1 1 130 80 15 16 13.0 14.00 9.30
183 0 42 50.0 152.000 NA 11 73 18 10.80 2 5 18.0 0 3 1.990 1.990 8.010 4.390 NA 36 34 NA 1.890 1.350 4.27 14.500 0.320 91.0 0 1 0 0 1 0 0 110 80 1 1 11.0 12.00 5.50
184 0 36 64.0 150.000 NA 13 72 18 12.50 2 5 13.0 1 0 323.000 236.500 4.320 1.600 NA 38 35 NA 2.260 2.380 14.18 63.300 0.290 100.0 1 1 0 1 1 0 0 120 80 3 4 13.0 12.00 9.00
185 0 26 60.0 164.500 NA 14 73 18 12.10 2 6 4.0 1 0 896.600 896.600 5.380 4.350 NA 38 32 NA 2.560 5.780 12.56 15.200 0.170 108.0 0 0 0 1 1 1 0 120 80 4 5 10.0 13.00 9.00
186 0 27 54.0 158.400 NA 15 72 18 11.50 2 5 5.0 0 0 1.990 1.990 2.630 1.120 NA 36 30 NA 1.000 4.660 23.95 41.000 0.250 100.0 0 0 1 1 0 1 1 110 80 5 7 13.0 14.00 6.50
187 1 32 66.0 155.000 NA 17 74 18 12.50 4 5 10.0 0 1 2.580 2.580 2.000 1.240 NA 39 35 NA 1.680 1.990 60.20 18.300 0.250 93.0 1 1 0 1 0 1 0 120 80 7 8 13.0 12.00 5.90
188 0 43 66.3 157.000 NA 14 75 18 11.70 2 5 19.0 1 2 569.300 569.300 3.760 3.180 NA 38 34 NA 1.720 1.280 2.51 46.900 0.630 91.0 1 0 0 1 0 0 0 110 80 2 3 11.0 12.00 9.70
189 0 28 76.0 154.000 NA 16 72 20 12.50 2 6 8.0 1 0 108.660 108.660 6.530 3.620 NA 40 34 NA 2.920 3.990 6.72 21.000 0.570 116.0 0 1 0 1 0 1 0 120 80 1 2 13.0 13.00 6.30
190 1 23 58.0 159.000 NA 15 74 18 10.80 4 4 2.5 0 0 1.990 1.990 3.260 2.190 NA 37 34 NA 0.550 5.690 25.35 8.600 0.190 133.0 1 1 0 1 1 1 1 110 80 7 9 16.0 15.00 13.00
191 0 34 54.0 153.000 NA 13 73 18 12.80 4 4 12.0 0 0 1.990 1.990 6.890 4.750 NA 36 30 NA 1.610 7.810 14.76 54.010 0.110 98.0 0 1 0 0 1 0 1 120 80 6 6 11.0 12.00 6.00
192 1 29 63.0 153.000 NA 17 74 18 11.00 4 3 8.0 0 0 3.990 3.990 3.630 1.020 NA 39 35 NA 2.660 6.410 29.08 6014.660 0.250 123.0 1 1 1 1 1 1 0 120 70 14 16 16.0 17.00 9.00
193 0 30 67.0 167.000 NA 12 72 22 12.00 4 5 10.0 1 1 455.800 121.800 5.700 2.370 NA 37 32 NA 2.360 5.760 10.79 28.700 0.250 108.0 0 1 0 0 1 0 1 110 80 2 4 10.0 13.00 6.00
194 0 30 55.0 173.700 NA 11 72 20 13.80 2 5 8.0 0 0 2.010 1.990 5.640 4.450 NA 34 32 NA 10.220 1.680 20.63 29.100 0.890 91.0 0 0 0 1 1 0 0 120 80 2 2 10.0 11.00 7.00
195 0 29 50.0 152.000 NA 13 74 18 10.80 4 3 6.0 1 0 122.580 122.580 5.490 7.060 NA 36 34 NA 2.800 8.750 25.73 57.080 0.400 102.0 0 1 0 0 1 1 0 120 80 5 6 13.0 12.00 7.00
196 1 35 60.0 153.400 NA 13 72 20 13.20 4 4 14.0 0 1 1.990 1.990 22.000 3.390 NA 38 35 NA 2.420 6.650 16.34 5418.600 0.310 100.0 1 0 1 1 1 1 0 120 80 8 10 15.0 13.00 5.50
197 1 35 56.0 156.000 NA 14 73 18 12.10 2 4 15.0 0 0 2.100 1.990 2.557 1.170 NA 38 34 NA 3.030 4.150 26.18 6.077 0.470 138.0 1 1 0 1 1 1 1 120 80 12 11 14.0 14.00 5.60
198 0 28 70.0 158.000 NA 15 72 18 12.00 2 5 6.0 0 0 1.990 1.990 5.300 2.720 NA 39 35 NA 2.490 1.860 10.87 61.580 0.790 90.0 1 1 0 1 0 1 0 110 80 5 7 12.0 14.00 9.00
199 0 32 49.0 154.000 NA 11 72 20 11.20 2 5 12.0 1 1 689.100 355.280 7.090 3.810 NA 35 32 NA 1.361 2.040 23.24 47.360 0.250 92.0 0 0 0 1 1 1 0 110 70 6 4 10.0 14.00 6.00
200 1 25 58.0 160.000 NA 15 74 18 11.90 4 3 3.0 0 0 1.990 1.990 3.800 3.570 NA 38 36 NA 3.900 7.250 30.78 55.600 0.250 90.0 1 1 1 1 0 1 0 110 80 12 12 14.0 17.00 10.50
201 0 30 47.0 158.000 NA 15 73 18 11.20 2 6 10.0 0 0 3.440 1.990 8.990 3.100 NA 36 32 NA 1.710 1.040 28.67 22.000 0.240 91.0 0 0 0 1 1 1 1 120 8 2 1 11.0 14.00 6.00
202 0 28 56.0 158.000 NA 13 72 20 10.80 2 5 8.0 1 0 122.300 122.300 5.200 4.970 NA 37 35 NA 2.740 1.910 14.00 9.010 0.140 100.0 1 0 0 1 0 1 0 120 80 2 4 12.0 12.00 9.80
203 0 28 80.0 163.000 NA 15 74 20 11.80 2 6 6.0 1 0 596.200 596.200 3.080 4.080 NA 40 36 NA 1.710 2.300 10.30 62.000 0.900 116.0 1 1 1 1 1 1 0 120 80 3 2 12.0 14.00 9.90
204 1 23 49.0 145.000 NA 14 73 18 13.20 4 4 4.0 0 0 1.990 1.990 4.070 2.590 NA 36 34 NA 2.040 5.610 10.22 15.360 0.790 108.0 1 1 0 1 0 1 0 110 80 14 16 17.0 15.00 10.60
205 0 30 55.0 162.000 NA 12 72 18 11.20 2 6 10.0 0 0 4.600 2.800 6.060 3.790 NA 37 35 NA 2.260 3.020 30.71 48.630 0.410 91.0 0 0 0 1 1 1 1 120 70 5 6 12.0 15.00 8.50
206 1 28 62.0 160.000 NA 15 73 18 12.00 4 5 7.0 0 0 2.100 1.990 3.440 0.770 NA 39 35 NA 0.750 5.250 23.12 43.680 0.320 140.0 1 1 1 0 1 1 1 110 80 15 18 15.0 19.00 8.00
207 1 24 83.0 161.000 NA 15 75 18 12.60 4 4 2.5 0 0 2.500 1.990 3.300 0.790 NA 42 38 NA 2.590 2.380 30.33 63.100 0.320 110.0 1 1 1 1 1 1 0 120 80 16 15 12.0 14.00 5.50
208 0 32 60.0 145.000 NA 15 73 18 11.20 4 5 12.0 1 2 2054.300 588.700 2.470 1.430 NA 37 34 NA 3.640 7.000 11.76 87.200 0.250 108.0 1 0 0 1 1 0 0 120 80 3 4 14.0 12.00 7.50
209 0 27 51.0 150.000 NA 13 72 18 12.01 2 6 7.0 1 0 147.600 147.600 5.790 3.570 NA 36 33 NA 1.220 3.170 25.04 90.000 0.660 84.0 0 0 0 1 1 0 0 110 80 5 7 12.0 13.00 6.50
210 1 35 62.0 146.000 NA 17 74 20 13.40 4 4 12.0 0 1 1.990 1.990 1.590 0.600 NA 38 35 NA 1.580 5.570 12.37 46.580 1.100 98.0 1 1 0 1 1 1 1 120 80 8 10 13.0 15.00 5.50
211 1 32 54.0 145.000 NA 11 72 18 10.80 4 5 10.0 0 0 2.200 1.990 3.130 0.420 NA 37 34 NA 1.450 4.570 31.96 21.200 0.360 102.0 1 0 1 1 0 1 0 120 80 12 15 15.0 18.00 6.10
212 1 39 55.0 150.000 NA 15 78 20 10.70 4 4 13.0 0 0 12.370 12.370 5.400 1.950 NA 34 32 NA 1.000 0.370 21.88 18.600 0.460 94.0 0 1 1 0 0 1 0 120 80 12 11 17.0 14.00 15.00
213 0 27 52.0 150.000 NA 11 82 20 11.10 2 5 5.0 0 0 1.990 1.990 4.970 2.230 NA 42 38 NA 1.940 16.900 9.12 23.600 0.250 93.0 0 0 0 1 1 0 1 120 80 1 1 17.0 16.00 9.40
214 1 26 70.0 150.000 NA 15 72 20 12.00 4 5 3.0 1 0 144.630 144.630 3.630 8.340 NA 36 34 NA 2.290 26.400 29.72 22.400 0.380 93.0 1 1 0 0 1 1 0 110 80 8 7 17.0 14.00 6.00
215 0 29 63.0 152.000 NA 11 72 18 13.20 2 6 11.0 1 4 25000.000 475.040 1.990 1.610 NA 38 34 NA 1.770 5.960 21.95 22.700 0.460 95.0 1 0 0 1 1 1 0 110 80 6 7 16.0 14.00 5.00
216 0 41 71.0 160.000 NA 15 74 20 11.00 4 3 16.0 0 1 1.990 1.990 4.740 3.600 NA 40 36 NA 1.090 9.100 22.62 31.600 0.250 85.0 1 0 0 1 1 1 1 120 80 9 7 14.0 14.00 10.00
217 1 36 66.0 162.000 NA 15 72 20 11.00 2 5 13.0 1 2 515.530 515.530 1.630 0.170 NA 36 34 NA 1.860 6.600 25.00 0.000 0.250 93.0 1 1 1 1 1 1 1 120 80 14 6 15.0 13.00 9.70
218 1 34 72.0 158.000 NA 13 72 18 11.00 4 4 13.0 0 1 1.990 99.690 4.760 2.700 NA 38 35 NA 2.300 22.000 12.46 22.700 0.250 93.0 1 1 0 1 1 1 0 110 80 9 12 15.0 13.00 10.00
219 0 26 47.8 158.000 NA 13 72 20 10.20 2 6 7.0 1 0 70.420 70.420 6.280 4.040 NA 34 32 NA 2.110 1.900 11.83 23.800 0.250 120.0 0 0 0 1 1 0 0 120 80 6 6 17.0 16.00 10.20
220 0 27 76.8 155.000 NA 13 78 20 11.10 4 4 1.0 0 0 342.910 342.910 7.260 5.330 NA 37 35 NA 6.070 4.300 23.54 21.800 0.250 93.0 1 0 0 1 1 1 0 120 80 3 2 14.0 15.00 14.00
221 0 39 61.0 152.000 NA 11 72 18 11.00 2 4 20.0 0 3 1.990 1.990 8.910 1.490 NA 36 34 NA 2.310 0.370 14.22 25.500 0.250 93.0 0 0 0 1 0 0 1 120 80 1 1 16.0 12.00 10.00
222 1 31 31.0 158.000 NA 15 80 20 12.00 4 3 3.0 0 0 1.990 1.990 3.990 6.630 NA 32 28 NA 1.300 17.600 13.91 19.570 0.250 133.0 0 1 1 1 0 1 0 120 70 7 6 14.0 16.00 10.00
223 1 27 65.0 158.000 NA 17 78 20 11.10 2 5 5.0 1 0 148.520 148.520 4.960 1.750 NA 35 32 NA 2.200 1.100 12.11 25.700 0.350 93.0 1 1 1 0 1 1 0 120 70 14 10 16.0 15.00 8.00
224 0 30 62.0 169.000 NA 11 18 20 12.00 2 5 5.0 0 0 1.990 1.990 7.040 4.090 NA 38 34 NA 1.540 7.800 19.60 31.800 0.250 95.0 0 0 0 1 0 1 1 120 70 4 3 14.0 18.00 8.70
225 0 36 62.0 152.000 NA 11 70 18 10.00 2 6 11.0 0 0 1.990 1.990 4.590 0.580 NA 38 36 NA 2.670 2.900 45.46 18.700 0.350 106.0 1 0 1 1 0 1 0 110 80 6 6 17.0 16.00 7.70
226 0 32 52.0 150.000 NA 13 70 18 9.40 2 5 12.0 0 0 272.780 272.780 4.330 2.110 NA 35 33 NA 2.100 7.700 40.47 41.000 0.250 93.0 0 0 0 1 0 0 1 110 80 6 7 16.0 15.00 12.00
227 0 35 61.0 158.000 NA 13 72 18 10.50 2 6 13.0 1 1 355.510 355.510 3.710 2.670 NA 36 34 NA 2.970 9.700 20.35 15.000 0.250 93.0 0 1 0 0 1 1 1 120 80 12 13 14.0 17.00 13.00
228 1 29 75.0 163.000 NA 15 72 18 10.70 2 6 3.0 0 0 1.990 1.990 3.270 1.560 NA 37 35 NA 2.380 0.200 34.73 10.100 0.430 85.0 1 1 1 1 1 1 0 110 80 1 3 11.0 12.00 6.00
229 0 31 61.0 147.000 NA 13 74 20 12.30 2 4 10.0 0 2 1.990 1.990 7.800 1.540 NA 40 38 NA 2.060 2.500 10.68 10.400 0.250 83.0 1 0 0 1 1 1 1 120 80 2 4 16.0 12.00 10.80
230 0 28 74.3 154.000 NA 13 72 18 10.70 2 5 6.0 0 3 1.990 1.990 3.700 4.600 NA 38 36 NA 1.700 12.000 46.25 10.800 0.770 85.0 1 0 1 1 0 1 0 110 80 6 5 14.0 13.00 8.50
231 1 40 71.0 152.000 NA 15 72 18 13.20 2 5 7.0 0 0 1.990 1.990 2.780 0.980 NA 38 35 NA 3.780 1.400 29.03 20.800 0.360 160.0 1 1 0 1 0 1 0 120 80 4 2 17.0 10.00 12.00
232 0 22 78.0 154.000 NA 15 72 18 12.40 2 5 9.0 0 1 150.910 150.910 5.210 2.190 NA 40 36 NA 1.240 16.700 10.02 20.800 0.270 77.0 1 0 0 0 1 0 0 120 80 1 1 13.0 10.00 8.80
233 1 35 55.0 162.000 NA 13 72 18 12.40 4 4 7.0 0 0 451.960 1.990 2.630 1.050 NA 35 32 NA 2.080 13.600 28.85 32.200 0.340 86.0 0 1 1 0 1 1 0 120 80 16 14 15.0 18.00 11.50
234 0 31 50.0 152.000 NA 15 72 18 10.00 2 6 4.5 0 1 392.730 1.990 5.370 8.150 NA 34 30 NA 25.910 16.800 14.41 23.910 0.250 92.0 0 0 0 0 1 1 1 120 80 7 5 17.0 15.00 9.00
235 0 27 45.0 152.000 NA 13 72 18 11.50 2 6 4.0 0 0 391.460 391.460 1.850 0.100 NA 32 30 NA 3.790 3.500 29.32 22.300 0.250 73.0 0 1 1 0 1 0 1 120 80 1 3 14.0 16.00 8.50
236 0 28 58.1 153.000 NA 13 72 18 12.40 2 5 4.0 0 0 418.820 1.990 7.500 1.480 NA 36 34 NA 0.730 1.300 27.73 31.100 0.250 86.0 0 1 0 0 0 1 1 110 70 0 5 17.0 18.00 4.80
237 0 33 54.0 146.000 NA 15 72 18 12.80 2 3 8.0 0 1 1.990 1.990 3.210 0.570 NA 38 35 NA 10.960 3.140 44.96 28.700 0.250 80.0 0 0 0 0 1 1 1 120 80 5 5 18.0 20.00 7.00
238 1 25 42.0 154.000 NA 14 72 18 12.50 4 3 4.0 0 0 1.990 1.990 6.580 3.090 NA 33 30 NA 0.850 1.250 12.81 30.200 0.670 100.0 1 0 0 1 1 0 0 120 80 6 5 15.0 17.00 9.00
239 0 32 66.0 164.000 NA 15 72 18 11.10 4 4 13.0 0 0 464.120 464.120 3.840 1.530 NA 34 31 NA 0.950 7.700 34.94 16.700 0.250 98.0 0 0 1 0 1 1 1 120 80 5 5 17.0 19.00 9.00
240 0 20 56.0 159.000 NA 13 72 18 12.40 2 5 1.5 1 0 389.260 41.770 5.900 3.130 NA 35 33 NA 0.610 7.300 10.60 22.000 0.250 86.0 0 0 0 0 1 0 1 120 80 10 5 15.0 18.00 8.00
241 1 24 53.6 166.000 NA 15 72 18 10.70 2 5 3.0 1 0 1390.580 1390.580 3.900 0.990 NA 34 32 NA 5.000 7.200 40.28 30.700 0.260 86.0 0 1 0 1 1 0 0 110 80 12 9 15.0 18.00 8.00
242 0 23 60.0 157.000 NA 17 72 18 11.50 2 6 2.0 0 0 1.990 1.990 6.090 2.150 NA 38 35 NA 1.370 3.290 29.11 26.000 0.250 93.0 0 0 0 1 1 0 0 110 80 6 4 16.0 19.00 14.00
243 0 37 65.0 159.000 NA 13 72 20 12.60 2 5 10.0 0 0 1.990 1.990 1.320 0.990 NA 36 33 NA 2.000 2.690 15.20 35.800 0.362 113.0 1 1 0 1 0 1 0 120 80 6 5 15.0 18.00 9.00
244 1 29 63.0 152.000 NA 15 72 18 11.50 2 6 2.0 0 0 213.830 213.830 3.180 1.000 NA 40 37 NA 2.470 2.100 29.62 31.600 0.250 92.0 1 1 1 1 0 1 0 120 80 4 7 10.0 15.00 9.20
245 1 39 104.0 164.400 NA 15 72 18 12.00 2 6 11.0 0 1 1.990 1.990 6.400 2.260 NA 46 44 NA 5.000 4.100 13.39 32.600 0.250 106.0 1 1 1 1 1 1 0 120 80 9 7 16.0 13.00 9.60
246 1 23 57.0 153.000 NA 15 73 18 12.00 2 4 3.0 1 0 353.600 45.900 3.180 1.290 NA 38 35 NA 0.900 6.200 23.09 30.100 0.250 106.0 0 0 1 1 1 1 1 120 80 8 10 17.0 18.00 9.00
247 0 26 54.0 163.000 NA 15 74 20 10.00 2 5 2.0 0 0 30.600 18.360 4.100 2.040 NA 36 34 NA 0.300 14.600 13.27 12.200 0.250 93.0 0 0 0 1 0 1 1 120 70 1 2 14.0 16.00 8.00
248 1 24 79.0 165.000 NA 16 72 18 10.20 2 5 4.0 0 0 154.480 154.480 3.070 1.870 NA 42 38 NA 4.070 4.710 31.40 24.000 0.250 86.0 1 1 1 0 1 1 0 110 80 10 15 18.0 19.00 10.30
249 1 33 65.0 153.000 NA 17 72 18 12.00 4 5 1.0 0 0 1.990 1.990 5.420 7.850 NA 38 36 NA 1.310 1.250 111.74 12.800 0.250 146.0 1 1 1 0 1 1 0 110 70 7 10 16.0 19.00 12.00
250 0 29 52.0 157.000 NA 15 72 18 14.20 2 4 1.5 0 1 1.990 1.990 4.700 1.600 NA 35 33 NA 0.290 1.000 23.79 39.700 0.250 94.0 0 0 0 1 0 0 0 120 70 4 6 18.0 17.00 11.00
251 1 45 85.0 153.000 NA 15 72 18 12.00 4 4 18.0 0 0 50.430 1.990 65.400 0.200 NA 44 40 NA 2.960 11.100 21.70 18.700 0.250 86.0 1 1 1 1 1 1 0 120 80 10 15 15.0 17.00 8.30
252 0 30 63.8 148.000 NA 11 72 18 11.50 4 3 7.0 0 0 1.990 1.990 7.380 3.460 NA 40 37 NA 2.930 1.900 15.28 21.200 0.250 80.0 0 0 0 0 0 1 1 120 70 0 1 17.0 15.00 13.00
253 1 47 62.7 154.000 NA 15 72 18 10.00 4 4 30.0 1 2 25000.000 25000.000 1.880 0.250 NA 38 36 NA 2.470 6.200 31.47 12.100 0.250 92.0 1 1 1 1 0 0 0 110 80 14 11 16.0 19.00 9.30
254 0 36 46.0 150.000 NA 15 72 18 13.30 2 5 18.0 0 0 1.990 1.990 1.580 0.260 NA 34 32 NA 4.860 1.900 26.08 26.300 0.250 105.0 0 0 0 0 1 0 0 110 80 1 3 18.0 14.00 6.00
255 0 28 52.0 156.000 NA 13 72 16 11.20 2 5 11.0 0 1 41.250 1.990 8.940 3.540 NA 34 32 NA 1.760 1.500 19.23 29.500 0.250 100.0 0 0 0 0 1 0 0 120 70 2 3 15.0 18.00 8.00
256 0 36 32.0 154.000 NA 15 72 20 12.50 2 6 15.0 0 0 1.990 638.520 1.320 0.950 NA 32 30 NA 0.920 2.900 5.27 31.500 0.400 80.0 0 0 0 1 1 0 0 120 80 5 7 16.0 17.00 8.20
257 0 33 35.0 150.000 NA 13 74 20 12.00 2 5 10.0 0 1 413.620 1.990 3.880 0.470 NA 30 28 NA 1.420 2.250 29.89 27.600 0.250 107.0 0 0 0 1 1 0 0 120 70 4 1 18.5 19.00 8.50
258 0 27 48.0 155.448 NA 15 72 18 12.50 2 6 6.5 0 0 1.990 1.990 1.760 0.100 NA 33 30 NA 1.170 6.800 22.51 13.700 0.400 72.0 0 0 0 1 0 1 0 110 80 2 5 17.0 18.00 9.00
259 0 43 33.0 152.000 NA 15 72 18 11.10 2 6 6.0 0 0 221.280 1.990 12.280 2.520 NA 30 28 NA 1.250 0.800 10.99 22.400 0.250 100.0 0 0 0 1 1 1 1 120 80 9 8 18.0 19.00 10.00
260 1 31 50.0 158.496 NA 11 74 20 12.50 2 5 4.0 0 0 227.360 1.990 2.120 0.370 NA 34 32 NA 5.010 7.210 27.01 32.100 0.250 100.0 0 1 1 1 0 1 0 120 80 10 15 17.0 18.00 8.00
261 0 38 34.0 153.000 NA 13 72 18 14.80 2 5 8.0 0 1 1.990 1.990 2.950 0.490 NA 32 30 NA 2.610 4.200 30.24 24.600 0.390 100.0 0 0 0 1 0 1 0 130 80 1 3 15.0 17.00 8.40
262 0 32 35.0 150.000 NA 11 70 18 11.50 2 4 4.0 0 0 643.410 1.990 1.700 0.460 NA 32 30 NA 2.840 6.200 19.27 20.900 0.250 72.0 0 0 0 0 0 1 0 110 80 1 0 19.0 20.00 10.00
263 0 38 53.5 158.496 NA 13 72 18 12.50 2 5 14.0 0 0 1.990 1.990 1.800 0.490 NA 35 33 NA 1.560 0.900 45.78 23.500 0.250 87.0 0 0 0 0 0 0 0 110 70 4 1 17.0 13.00 6.50
264 0 37 45.0 156.000 NA 17 74 20 12.10 2 5 10.0 0 0 3.250 4.760 10.390 4.110 NA 35 32 NA 1.710 16.800 33.58 43.900 0.250 100.0 0 0 0 1 1 0 0 120 80 5 5 19.0 18.00 8.70
265 0 34 35.0 155.000 NA 13 74 20 12.40 2 4 8.0 0 0 165.610 1.990 4.940 2.300 NA 32 29 NA 3.360 16.900 23.62 16.400 0.260 88.0 0 0 0 1 1 0 0 110 70 10 7 18.0 18.00 9.00
266 0 40 32.0 152.000 NA 15 72 20 10.00 2 3 2.0 0 1 1.990 1.990 5.010 1.600 NA 30 28 NA 5.000 8.500 23.30 27.700 0.270 88.0 0 0 0 0 0 1 1 120 80 6 5 18.0 17.00 8.00
267 0 25 64.4 157.000 NA 11 72 18 11.50 2 5 4.0 0 0 92.920 1.990 6.930 2.860 NA 34 32 NA 4.000 3.900 21.92 30.800 0.400 84.0 0 0 0 1 1 0 0 110 80 3 2 19.0 18.00 5.60
268 1 28 58.9 159.000 NA 11 72 20 12.40 2 6 1.0 0 0 117.140 18.130 7.350 3.610 NA 36 34 NA 1.710 66.000 15.37 30.200 0.440 70.0 0 1 1 1 0 1 1 120 80 7 10 19.0 19.00 10.00
269 0 25 53.5 152.000 NA 11 72 18 12.40 4 3 3.0 0 0 100.580 1.990 5.460 0.910 NA 32 30 NA 3.250 26.800 33.07 21.400 0.250 70.0 0 0 0 1 0 0 0 110 80 6 5 17.0 19.00 7.00
270 1 26 59.0 154.000 NA 17 70 18 10.50 2 4 3.0 0 0 1.990 1.990 1.000 0.100 NA 35 33 NA 0.590 1.150 35.06 20.700 0.250 100.0 1 1 1 1 1 1 0 110 80 9 4 20.0 16.00 13.00
271 0 30 55.1 167.640 NA 11 72 18 12.00 2 5 8.5 0 0 127.040 1.990 7.860 2.230 NA 34 32 NA 1.540 4.100 24.01 22.100 0.250 83.0 1 1 1 1 1 1 0 110 80 7 6 19.0 18.00 8.00
272 0 26 55.7 162.000 NA 17 72 18 11.00 2 6 1.0 0 0 164.910 1.990 6.120 3.250 NA 36 33 NA 4.750 3.200 15.63 31.600 0.250 84.0 0 0 0 0 1 0 1 110 70 2 3 18.0 18.00 8.70
273 0 43 40.0 152.000 NA 15 72 18 11.50 2 6 25.0 0 0 1.990 1.990 14.990 7.090 NA 30 28 NA 6.090 0.160 24.78 24.500 0.300 100.0 0 0 0 0 1 0 1 120 70 4 4 17.0 18.00 8.30
274 0 30 52.0 152.000 NA 13 78 20 11.10 2 4 6.0 1 0 785.440 1.990 6.300 6.260 NA 32 30 NA 2.580 8.100 38.24 26.300 0.250 112.0 0 0 0 1 1 0 1 120 80 8 7 18.0 16.00 7.00
275 0 30 58.9 153.000 NA 13 72 18 12.00 2 5 4.0 0 0 1.990 1.990 5.590 3.850 NA 35 33 NA 3.000 0.560 128.24 30.300 0.350 88.0 0 0 0 0 0 0 1 120 80 4 6 17.0 17.00 10.30
276 1 27 47.0 152.000 NA 15 74 20 10.70 2 5 8.0 0 0 355.080 1.990 6.800 4.610 NA 33 28 NA 3.490 5.300 14.65 26.100 0.250 90.0 0 0 0 0 0 0 1 120 70 12 15 16.0 18.00 8.70
277 1 36 65.7 151.000 NA 15 72 20 11.00 4 4 8.0 0 1 89.340 89.340 7.470 6.640 NA 37 35 NA 5.000 1.800 8.00 19.900 0.250 92.0 1 0 0 1 1 1 0 120 80 6 5 18.0 16.00 8.80
278 1 29 66.0 150.000 NA 15 72 18 12.00 4 5 9.0 0 0 1.990 1.990 2.560 2.410 NA 36 34 NA 2.860 7.300 12.91 19.000 0.390 92.0 0 1 1 1 0 0 1 110 70 1 1 12.0 14.00 9.50
279 1 45 50.0 154.000 NA 17 72 18 10.20 4 4 11.0 0 2 412.900 366.040 2.240 1.990 NA 30 27 NA 22.590 6.500 16.84 10.500 0.500 138.0 1 1 1 1 1 1 0 120 80 4 8 15.0 18.00 7.20
280 0 26 54.0 150.000 NA 11 72 18 12.00 4 3 5.0 0 0 184.800 1.990 5.810 3.910 NA 36 34 NA 2.350 21.000 29.69 20.100 0.480 125.0 1 1 1 1 0 1 0 120 70 3 2 15.0 13.00 9.00
281 0 31 65.0 158.000 NA 15 72 18 12.40 2 5 4.0 0 0 1.990 1.990 2.950 0.260 NA 38 36 NA 3.230 0.900 17.19 25.500 0.660 92.0 0 1 0 1 1 0 1 120 70 3 3 18.0 15.00 8.90
282 0 31 51.0 155.000 NA 17 70 18 11.00 4 5 5.0 0 0 1.990 1.990 60.370 9.990 NA 33 30 NA 1.000 15.300 20.47 25.600 0.330 92.0 0 0 0 0 1 0 1 110 80 4 5 16.0 19.00 7.00
283 1 38 62.0 154.000 NA 15 74 20 10.50 4 4 5.0 1 0 454.270 14.340 8.150 3.260 NA 35 33 NA 7.020 10.600 22.90 16.700 0.480 92.0 1 0 0 1 1 1 0 120 80 8 5 15.0 17.00 9.80
284 0 32 68.0 158.000 NA 17 72 18 10.50 2 6 13.0 0 0 3.100 75.620 2.350 0.290 NA 37 35 NA 5.000 4.200 38.51 24.000 0.700 100.0 1 0 0 0 1 1 1 110 80 3 5 17.0 16.00 9.80
285 0 32 46.0 152.000 NA 15 72 20 11.50 2 6 8.0 0 0 1.990 1.990 4.790 4.500 NA 32 30 NA 2.000 4.700 96.63 22.600 0.500 112.0 0 0 0 1 1 0 0 120 80 1 1 11.0 16.00 11.00
286 0 38 52.0 156.000 NA 17 72 18 10.80 2 5 10.0 0 0 227.560 4.960 3.750 0.340 NA 33 30 NA 1.260 4.800 17.07 20.100 0.300 110.0 0 0 0 1 0 0 1 120 80 1 2 20.0 17.00 7.20
287 0 26 67.0 154.000 NA 11 72 18 13.20 2 5 5.0 0 1 1.990 1.990 7.760 2.710 NA 36 34 NA 3.710 1.700 23.98 17.100 0.250 122.0 1 0 0 1 0 1 0 120 70 2 1 16.0 14.00 7.80
288 1 29 53.0 152.000 NA 17 78 22 11.00 2 5 2.5 0 0 53.090 1.990 6.060 2.140 NA 34 32 NA 2.350 5.400 12.26 18.000 0.250 100.0 0 1 1 0 1 0 0 110 80 10 7 18.0 14.00 9.00
289 0 35 66.0 155.000 NA 15 78 22 10.50 4 3 4.0 0 2 1.990 1.990 6.510 3.230 NA 37 35 NA 4.610 1.200 18.14 27.200 0.310 100.0 1 0 0 0 0 1 0 110 80 1 0 15.0 18.00 7.70
290 0 27 65.0 162.000 NA 15 72 18 10.70 2 5 3.5 0 1 213.210 1.990 5.470 4.950 NA 38 36 NA 4.290 5.400 41.03 21.400 0.250 92.0 1 1 1 1 1 0 0 110 80 5 3 16.0 17.00 8.00
291 1 27 83.0 164.000 NA 13 70 18 11.00 2 6 2.0 0 0 1.990 1.990 4.600 2.490 NA 39 36 NA 6.480 17.500 12.88 26.600 0.500 92.0 0 0 0 1 1 0 0 110 80 1 1 14.0 15.00 8.50
292 0 35 53.0 158.000 NA 17 74 20 12.40 2 6 11.0 0 1 1.990 1.990 8.990 4.880 NA 35 33 NA 1.320 6.000 17.00 30.500 0.400 110.0 1 0 0 0 1 1 0 120 80 1 3 16.0 17.00 9.00
293 0 30 68.0 158.000 NA 15 72 18 10.50 2 4 10.0 0 0 1.990 1.990 1.760 0.200 NA 37 35 NA 0.940 2.000 43.63 35.800 0.410 92.0 0 1 1 1 0 0 1 120 70 2 2 12.0 16.00 10.50
294 1 27 36.0 153.000 NA 13 70 18 11.40 2 5 6.5 1 0 1134.400 1134.400 4.250 5.110 NA 30 28 NA 2.080 21.800 19.31 23.300 0.410 79.0 1 1 1 0 1 1 0 110 80 11 12 13.0 15.00 5.50
295 1 28 55.5 144.000 NA 13 72 20 11.80 2 5 6.5 1 2 785.950 785.950 4.000 2.950 NA 38 35 NA 0.860 18.500 7.49 20.400 0.620 95.0 1 1 1 0 1 1 0 120 80 13 20 14.0 16.00 9.60
296 1 30 57.0 155.000 NA 13 72 18 12.00 2 5 10.0 0 0 291.430 1.990 5.470 3.000 NA 35 33 NA 6.540 12.400 39.60 23.900 0.940 106.0 0 0 0 0 0 1 1 110 70 9 10 18.0 19.00 8.00
297 0 31 50.0 155.000 NA 15 13 18 11.00 2 4 12.0 0 0 20.810 1.990 6.698 5.130 NA 33 30 NA 3.861 10.800 14.02 24.900 0.410 100.0 0 0 1 0 1 0 0 110 70 8 5 17.0 15.00 8.50
298 0 20 52.0 150.000 NA 15 72 18 10.50 2 6 3.0 0 0 1.990 1.990 4.870 3.610 NA 34 32 NA 2.930 4.500 20.18 27.000 0.340 88.0 0 0 0 1 1 1 0 110 70 7 9 13.0 17.00 9.60
299 0 34 67.0 152.000 NA 15 74 20 11.50 2 6 13.0 0 1 1.990 1.990 5.590 3.050 NA 37 35 NA 3.670 4.200 14.88 13.900 0.700 95.0 1 1 1 1 1 1 0 120 70 1 0 16.0 12.00 7.70
300 0 26 50.0 152.000 NA 11 72 20 11.00 4 5 2.0 0 0 1.990 1.990 5.700 4.500 NA 32 28 NA 1.240 1.200 38.70 23.200 0.260 92.0 0 0 1 0 0 1 1 120 80 1 0 15.0 18.00 11.00
301 1 40 85.0 173.000 NA 13 72 18 10.50 2 5 13.0 0 1 1.990 1.990 4.380 1.640 NA 38 36 NA 3.740 10.800 29.76 18.300 0.330 100.0 1 0 0 0 1 1 0 110 80 2 1 18.0 15.00 7.00
302 0 28 53.0 152.000 NA 11 72 18 11.20 2 4 7.5 0 1 110.780 1.990 5.710 2.830 NA 33 30 NA 4.690 10.000 22.10 30.300 0.400 100.0 1 1 1 1 0 1 0 110 80 7 5 16.0 13.00 9.00
303 0 26 64.0 153.000 NA 17 72 18 12.00 4 3 3.0 0 0 1.990 1.990 5.140 8.170 NA 34 31 NA 1.100 18.700 31.83 30.000 0.460 135.0 0 0 0 1 0 0 1 120 80 3 4 16.0 14.00 9.50
304 1 25 57.0 148.000 NA 15 72 20 10.00 2 5 1.0 0 0 1.990 1.900 2.520 1.410 NA 30 28 NA 3.040 18.000 35.03 31.200 0.560 80.0 1 1 1 1 1 0 0 110 80 10 12 15.0 14.00 9.00
305 0 35 52.0 150.000 NA 13 70 18 10.70 2 5 7.0 0 0 1.990 1.990 5.040 2.220 NA 34 32 NA 0.810 0.280 16.40 25.000 0.330 94.0 0 0 0 0 1 0 1 110 80 3 2 17.0 15.00 12.00
306 0 37 56.0 152.000 NA 13 74 20 11.70 2 5 9.0 0 0 42.000 1.990 2.910 0.350 NA 35 33 NA 16.000 NA 2.22 38.600 0.300 100.0 0 0 0 0 1 0 1 120 70 4 5 17.0 16.00 5.60
307 1 41 40.0 155.000 16.6 13 72 16 11.00 2 5 7.0 0 0 1.990 1.990 3.680 0.830 4.433 34 32 0.941 1.130 1.030 7.49 33.100 0.250 82.0 0 1 1 0 1 0 0 110 70 8 12 11.0 11.50 5.50
308 0 24 68.0 158.000 27.2 15 72 16 10.80 2 6 3.0 1 0 229.860 229.860 3.330 1.990 1.673 39 36 0.923 2.000 3.020 10.57 22.500 0.250 80.0 1 0 0 0 1 1 0 120 80 2 4 12.5 12.00 8.00
309 1 33 52.0 152.000 22.5 11 72 18 11.00 2 5 8.0 0 1 1.990 1.990 5.840 4.540 1.286 38 32 0.842 3.740 1.000 33.68 31.550 0.250 92.0 0 1 1 1 1 1 0 110 70 6 9 13.0 13.00 6.00
310 1 24 52.0 162.000 19.8 15 72 18 11.00 4 3 4.0 0 0 1.990 1.990 3.260 1.660 NA 39 32 NA 2.410 1.500 9.64 28.400 0.780 92.0 0 0 1 0 1 1 0 110 80 6 7 15.0 16.00 7.00
311 0 39 55.0 158.000 22.0 15 72 20 12.00 2 5 14.0 0 0 3.900 3.900 7.270 2.480 NA 38 34 NA 5.000 3.000 38.15 30.500 0.250 95.0 1 0 0 0 1 0 1 110 70 1 2 15.0 11.00 7.30
312 1 30 72.0 161.000 27.8 11 72 22 12.20 4 2 1.5 1 0 297.210 297.210 11.620 2.060 NA 45 38 NA 2.130 1.000 14.06 32.400 0.380 108.0 1 1 1 1 1 1 0 110 80 12 6 17.0 18.00 12.00
313 1 28 56.0 158.000 22.4 15 74 20 12.00 2 5 7.0 1 0 277.280 277.280 4.890 1.970 NA 39 32 NA 1.570 1.060 19.04 34.100 0.460 100.0 1 0 1 0 1 1 0 120 70 7 12 15.0 18.00 11.00
314 0 38 50.0 161.000 19.1 15 72 22 12.00 2 5 13.0 1 0 1.990 783.360 3.710 2.440 NA 38 32 NA 0.540 3.050 53.39 42.500 0.290 100.0 0 0 0 0 1 1 1 110 80 7 9 11.0 15.00 7.80
315 1 34 55.0 158.000 22.0 11 78 20 11.00 2 6 10.0 0 0 1.990 1.990 6.470 5.570 NA 36 32 NA 3.010 5.400 24.70 35.800 0.350 100.0 0 1 1 0 1 1 0 120 80 5 5 12.0 10.00 11.00
316 0 28 65.0 167.000 23.3 11 72 18 9.00 2 6 5.0 1 0 21084.210 21084.210 3.760 0.620 NA 38 34 NA 1.820 3.020 28.56 52.400 0.250 120.0 0 0 0 0 0 1 0 110 80 8 12 14.0 18.00 8.50
317 0 30 66.0 152.000 28.6 11 72 18 12.00 2 5 7.0 1 0 409.850 409.850 3.400 1.310 NA 40 36 NA 1.680 2.230 10.45 36.500 0.360 110.0 1 0 0 1 1 1 0 110 80 8 4 16.0 17.00 10.00
318 0 28 53.0 159.000 21.0 11 74 18 10.80 2 6 4.0 1 0 17243.970 410.130 4.890 0.580 NA 36 30 NA 1.140 2.060 21.19 22.200 0.270 80.0 0 0 0 1 1 1 0 100 70 4 6 14.0 18.00 7.70
319 0 47 60.0 164.500 22.2 13 72 18 13.00 2 5 24.0 1 0 479.600 479.600 4.590 1.400 NA 36 32 NA 5.890 3.330 13.78 35.400 0.410 100.0 0 0 0 1 1 1 0 110 80 6 6 15.0 18.00 8.40
320 0 32 60.0 154.000 25.3 15 72 18 13.80 2 5 5.0 1 0 545.780 545.780 6.030 3.240 NA 38 34 NA 3.280 1.000 22.14 25.100 0.380 85.0 1 0 0 1 1 1 0 120 80 1 2 16.0 17.00 6.40
321 1 33 64.0 162.000 24.4 13 72 18 10.20 2 5 13.0 1 0 783.980 1.990 3.170 1.450 NA 36 30 NA 0.820 2.650 18.13 33.500 0.250 82.0 0 1 1 1 1 1 0 110 80 7 20 15.0 17.00 12.00
322 0 38 50.0 150.000 22.2 17 72 18 12.00 4 3 12.0 0 0 1.990 1.990 4.300 2.620 NA 34 30 NA 2.420 11.000 32.23 29.400 0.260 80.0 0 0 0 1 1 1 0 110 80 5 11 8.0 12.00 10.50
323 0 28 61.0 150.000 27.1 11 72 24 10.80 2 6 6.5 0 0 1.990 1.990 7.030 2.300 NA 39 35 NA 0.220 3.600 26.89 32.400 0.350 85.0 1 0 0 0 1 1 0 110 80 7 11 15.0 18.00 8.00
324 1 37 56.0 155.000 23.3 17 78 22 12.00 4 2 14.0 1 0 320.490 320.490 2.060 0.790 NA 37 34 NA 4.620 5.700 63.30 35.700 0.250 110.0 1 0 1 1 1 1 0 110 80 14 19 18.0 19.00 12.00
325 0 40 64.0 158.000 25.6 13 74 28 10.80 2 5 17.0 0 0 1.990 1.990 6.200 5.000 NA 39 35 NA 7.270 1.030 35.66 27.600 0.310 100.0 1 0 0 0 1 1 1 110 80 4 7 10.0 16.00 9.00
326 0 35 57.0 158.000 22.8 15 80 22 10.60 2 6 17.0 1 0 12.000 12.000 4.080 1.320 NA 36 32 NA 4.020 6.330 26.01 23.400 0.290 85.0 0 0 0 1 1 1 1 110 70 4 4 10.0 15.00 11.00
327 0 45 54.0 152.000 23.4 13 72 16 10.60 2 6 23.0 0 3 3.010 78.380 5.270 3.050 NA 38 35 NA 3.690 2.500 22.29 37.900 0.480 100.0 0 0 0 1 1 0 1 120 80 2 4 10.0 15.00 13.00
328 0 31 55.0 161.000 21.2 11 74 18 10.20 2 5 11.0 1 0 204.690 204.690 3.830 4.790 NA 37 34 NA 0.450 2.200 20.32 32.600 0.340 85.0 0 0 0 1 1 0 1 110 70 4 3 18.0 13.00 9.00
329 1 30 65.0 161.000 25.1 11 70 18 11.00 4 2 7.0 1 0 336.950 12.000 6.860 14.240 NA 39 34 NA 2.320 15.700 8.78 18.700 0.320 100.0 1 1 1 0 1 1 0 110 80 7 10 13.0 17.00 8.00
330 0 29 53.0 161.000 20.4 15 72 18 10.20 2 6 5.0 1 0 900.600 900.600 5052.000 3.680 NA 38 35 NA 0.830 3.500 30.58 28.600 0.300 108.0 0 0 0 1 1 1 0 110 80 6 4 16.0 17.00 6.00
331 0 33 60.0 162.000 22.9 11 72 18 10.30 2 6 13.0 0 0 1.990 1.990 5.890 1.270 NA 38 35 NA 0.690 1.100 1.36 20.300 0.250 100.0 0 0 0 1 1 0 0 120 80 1 2 12.0 18.00 9.00
332 0 29 59.0 155.000 24.6 17 70 18 10.70 4 3 12.0 1 0 12.000 12.000 6.470 1.470 NA 38 34 NA 1.570 1.300 34.14 32.400 0.250 92.0 0 0 0 0 0 1 0 110 80 3 3 13.0 10.00 6.00
333 0 27 59.0 158.000 23.6 15 72 18 10.00 2 6 6.0 1 0 159.710 159.710 1.850 0.670 NA 36 32 NA 2.130 3.000 23.15 34.500 0.250 92.0 0 0 0 1 1 0 0 110 70 3 3 14.0 12.00 10.50
334 0 31 45.0 153.000 19.2 13 80 20 10.80 2 5 8.0 1 0 15.000 15.000 3.910 2.510 NA 35 30 NA 2.240 3.000 23.20 47.500 0.250 100.0 0 0 1 1 0 0 0 110 80 4 1 11.0 10.00 12.00
335 0 30 62.0 152.000 26.8 15 74 18 11.50 2 5 6.0 1 0 296.310 296.310 0.210 0.920 NA 38 35 NA 1.750 2.170 12.11 22.900 0.420 77.0 1 0 0 1 1 1 0 120 80 6 7 15.0 11.00 11.00
336 0 30 46.0 161.000 17.7 15 78 22 10.80 2 5 5.0 1 0 8012.990 4176.000 2.440 0.220 NA 34 31 NA 0.790 3.300 14.98 13.400 0.890 100.0 0 0 0 1 1 0 0 110 80 2 2 14.0 15.00 9.00
337 1 21 82.0 158.000 32.8 16 78 24 10.60 4 3 3.0 1 0 1.990 183.060 3.410 1.390 NA 44 38 NA 5.000 14.700 31.70 33.700 0.250 85.0 1 1 1 1 1 1 0 110 80 8 10 15.0 12.00 10.00
338 1 36 89.0 161.000 34.3 15 78 24 14.00 4 3 5.0 0 0 1.990 1.990 7.100 2.220 NA 44 40 NA 4.230 2.800 10.16 35.100 0.250 130.0 1 1 1 1 1 1 0 110 80 16 12 11.0 9.00 9.00
339 1 31 73.0 161.000 28.2 15 78 24 10.70 4 2 7.0 0 0 1.990 1.990 3.610 5.090 NA 42 38 NA 1.690 1.060 14.11 29.100 0.790 92.0 1 1 1 0 0 1 0 110 80 12 10 10.0 10.00 9.30
340 0 29 53.0 152.000 22.9 17 78 22 10.60 2 5 2.0 1 0 1.990 20.000 5.710 4.320 NA 38 34 NA 3.980 9.100 20.78 19.700 0.320 123.0 0 0 1 0 0 1 1 110 80 1 1 10.0 12.00 8.20
341 1 26 60.0 152.000 26.0 13 78 22 12.50 4 2 5.0 1 0 161.770 161.770 6.830 4.620 NA 40 36 NA 2.390 5.900 34.20 38.600 0.300 76.0 1 1 0 0 1 1 0 100 80 10 15 14.0 11.00 10.00
342 1 26 65.0 158.000 26.0 15 75 24 10.50 2 5 7.0 1 0 61.980 61.980 7.120 7.890 NA 40 35 NA 2.290 8.900 32.41 23.400 0.330 107.0 1 1 0 0 1 1 0 110 80 10 7 10.0 11.00 8.70
343 1 32 40.0 158.000 16.0 15 78 22 11.20 2 5 12.0 1 0 403.850 403.850 6.720 3.240 NA 36 32 NA 1.770 3.600 13.27 57.700 0.250 107.0 0 0 0 1 1 1 0 110 80 4 15 15.0 12.00 11.00
344 0 25 85.0 161.000 32.8 11 78 22 10.20 2 5 3.0 1 2 136.710 1.990 7.100 5.160 NA 42 38 NA 2.150 2.310 19.21 31.400 0.250 100.0 1 0 0 1 0 1 0 110 80 10 5 15.0 17.00 8.00
345 0 36 62.0 158.000 24.8 13 78 22 11.00 2 6 14.0 1 0 34.650 34.650 4.480 1.250 NA 40 36 NA 0.720 3.600 8.38 30.100 0.250 107.0 0 0 0 1 1 1 0 110 80 3 5 10.0 11.00 9.20
346 0 30 50.0 152.000 21.6 15 78 22 10.50 4 3 2.0 1 0 48.860 48.860 4.630 0.300 NA 38 35 NA 1.130 2.800 5.65 36.900 0.250 92.0 0 0 0 0 1 1 0 110 80 1 1 10.0 10.00 13.00
347 1 28 83.0 164.000 30.9 11 74 20 11.10 4 2 7.0 0 1 1.990 1.990 5.500 3.030 NA 42 37 NA 20.850 20.400 14.63 10.500 0.520 110.0 1 1 0 1 1 1 0 110 80 10 12 15.0 17.00 10.00
348 0 31 56.0 158.000 22.4 11 72 18 10.20 2 6 10.0 1 0 371.740 371.740 1.800 0.350 NA 38 35 NA 0.530 1.500 20.90 34.000 0.250 100.0 0 0 0 1 1 1 0 110 70 6 4 10.0 11.00 11.00
349 0 32 57.0 157.000 23.1 13 72 20 10.80 2 5 4.0 0 0 1.990 1.990 3.980 1.430 NA 38 34 NA 4.290 4.600 15.39 26.100 0.450 80.0 0 0 1 0 1 1 1 110 70 4 8 11.0 15.00 10.80
350 0 30 71.0 157.000 28.8 13 72 18 11.00 4 2 5.0 0 0 1.990 1.990 7.570 4.650 NA 42 36 NA 1.420 5.000 20.45 13.900 0.590 92.0 1 0 0 1 1 1 0 110 80 1 3 12.0 15.00 14.00
351 0 22 56.0 154.000 23.6 13 72 18 10.80 2 6 1.0 1 0 213.490 1.990 4.690 3.670 NA 39 34 NA 3.620 3.900 25.50 35.500 1.240 100.0 0 0 0 1 1 1 0 110 80 10 6 15.0 12.00 9.00
352 1 36 54.0 148.000 24.7 13 78 24 10.20 4 2 15.0 1 0 970.750 970.750 5.260 9.870 NA 38 33 NA 1.450 9.800 8.10 42.100 0.480 85.0 0 1 0 1 1 1 0 100 80 10 7 13.0 15.00 9.00
353 1 27 60.0 164.000 22.3 15 72 18 10.70 2 6 2.5 1 0 109.060 109.060 3.190 2.960 NA 40 35 NA 2.960 3.650 18.92 36.400 0.250 100.0 0 1 1 0 0 1 0 120 80 4 5 15.0 18.00 8.00
354 0 30 55.0 164.000 20.4 13 74 22 10.20 2 6 7.0 1 1 1082.820 1082.820 5.740 2.810 NA 37 34 NA 1.840 3.500 24.52 31.200 0.250 92.0 0 0 0 1 1 1 0 110 80 6 4 13.0 15.00 5.60
355 0 33 50.0 152.000 21.6 15 80 20 9.80 4 2 12.0 1 0 739.130 739.130 6.680 4.030 NA 38 35 NA 2.380 10.700 24.52 45.400 0.320 85.0 0 0 0 1 1 1 0 110 80 7 4 15.0 18.00 10.60
356 0 28 61.0 158.000 24.4 15 72 18 10.50 2 6 6.0 1 0 288.720 288.720 4.160 0.270 NA 40 35 NA 6.310 4.800 54.88 17.400 0.250 100.0 0 1 0 0 1 0 1 110 80 5 7 15.0 18.00 7.00
357 1 32 80.4 165.000 29.5 18 72 18 11.50 4 3 10.0 0 0 4.680 1.990 4.480 0.210 NA 42 38 NA 2.060 4.500 48.57 19.300 0.270 105.0 1 1 1 0 1 1 0 120 80 9 12 15.0 15.00 11.00
358 0 32 47.0 158.000 18.8 15 74 20 10.20 2 6 7.0 1 0 18.490 18.490 4.520 3.270 NA 37 33 NA 1.400 2.600 19.78 18.800 1.000 115.0 0 1 0 0 1 0 0 110 80 1 1 14.0 16.00 6.40
359 0 34 53.0 145.000 25.2 12 78 20 10.80 2 6 10.0 1 0 169.330 169.330 3.270 0.630 NA 38 35 NA 1.460 3.900 31.77 23.900 0.400 115.0 1 0 1 0 0 1 0 120 70 2 2 10.0 15.00 12.00
360 0 36 61.0 154.000 25.7 13 70 18 10.50 2 6 12.0 1 0 418.900 418.900 2.850 1.710 NA 38 35 NA 1.540 3.090 26.09 20.160 0.310 95.0 1 0 0 1 1 0 1 120 80 2 3 13.0 11.00 6.80
361 0 34 61.0 154.000 25.7 15 72 18 10.50 2 5 7.0 0 0 1.990 1.990 6.420 3.450 NA 39 34 NA 2.640 10.900 14.52 39.100 0.250 100.0 1 0 0 1 0 0 0 120 70 3 3 13.0 14.00 13.50
362 0 35 62.0 148.000 28.3 13 78 20 10.80 2 6 17.0 0 1 1.990 1.990 2.710 1.410 NA 40 36 NA 1.870 5.200 28.27 30.900 0.300 92.0 1 0 0 1 1 0 1 120 70 2 3 10.0 10.00 7.00
363 0 42 50.0 161.000 19.3 15 74 16 10.00 4 3 5.0 1 0 14.830 53.820 4.910 0.670 NA 37 32 NA 0.970 1.600 23.58 21.300 0.250 82.0 0 0 0 1 0 0 0 120 80 1 1 14.0 13.00 9.20
364 0 31 48.0 152.000 20.8 17 72 18 11.00 2 6 8.0 0 0 1.990 1.990 5.980 1.000 NA 35 30 NA 3.130 1.000 20.83 43.400 0.560 100.0 0 0 0 1 1 1 0 110 80 1 3 13.0 13.00 7.00
365 0 35 48.0 158.000 19.2 15 72 20 10.80 2 5 10.0 1 1 650.710 1.990 7.770 3.640 NA 36 30 NA 1.450 6.400 29.56 24.600 0.550 110.0 0 0 1 0 0 1 0 120 80 3 3 16.0 15.00 9.00
366 0 38 77.9 161.000 30.1 15 72 24 11.10 2 5 2.0 0 0 1.990 1.990 4.320 1.350 NA 42 37 NA 1.120 2.800 19.20 20.500 0.340 100.0 1 1 0 0 1 1 0 140 100 2 2 15.0 14.00 8.50
367 1 23 67.0 152.000 29.0 11 72 20 10.00 4 2 2.0 0 0 1.990 1.990 3.230 2.340 NA 38 32 NA 1.270 6.400 22.99 15.900 0.320 92.0 1 0 1 1 1 0 0 120 80 8 11 13.0 15.00 9.80
368 0 20 64.0 165.000 23.5 15 72 18 11.50 2 6 8.0 1 0 10.450 10.450 2.080 0.150 NA 38 35 NA 5.000 2.190 50.51 28.700 0.250 86.0 0 0 0 1 1 1 0 120 80 5 7 15.0 14.00 11.00
369 1 22 58.0 161.000 22.4 13 74 22 10.00 4 3 5.0 1 0 764.830 764.830 4.060 3.920 NA 38 34 NA 5.000 15.000 37.49 22.000 0.560 92.0 0 1 0 1 1 1 0 120 80 8 11 15.0 14.00 10.00
370 0 35 60.0 152.000 26.0 15 74 18 10.00 2 5 3.0 1 0 116.310 116.310 6.520 3.420 NA 39 36 NA 0.770 2.500 40.73 20.000 0.300 92.0 1 0 0 1 0 1 0 110 80 3 4 12.0 15.00 8.00
371 1 28 58.0 164.000 21.6 13 72 18 11.50 2 6 1.0 1 0 785.420 300.130 6.220 4.440 NA 37 34 NA 0.640 4.200 27.24 26.000 0.290 100.0 0 1 1 0 1 1 1 110 80 15 12 18.0 20.00 12.00
372 0 31 68.0 161.000 26.2 15 72 18 12.80 2 6 6.0 1 0 96.220 5.390 1.580 0.240 NA 39 35 NA 6.910 5.100 15.85 25.600 0.500 87.0 1 0 0 1 1 1 0 110 80 4 9 12.0 17.00 9.00
373 1 29 74.0 159.000 29.3 11 72 20 10.20 4 3 3.0 1 1 346.590 346.590 5.760 6.420 NA 40 37 NA 0.950 6.000 45.54 36.200 0.320 92.0 1 1 1 0 0 1 0 120 80 12 9 17.0 19.00 8.00
374 0 28 42.0 163.000 15.8 13 70 18 12.00 2 5 7.0 1 0 347.980 1.990 2.590 0.630 NA 35 30 NA 5.740 5.800 33.04 39.300 0.395 84.0 0 0 0 1 1 0 0 120 70 8 7 15.0 15.00 11.00
375 0 32 65.0 161.000 25.1 11 74 20 10.20 2 6 11.0 1 0 6.190 6.190 2.170 2.020 NA 38 34 NA 4.780 0.200 99.93 17.300 0.250 115.0 1 0 0 0 1 1 0 110 70 1 2 13.0 14.00 7.50
376 1 27 60.0 158.000 24.0 14 72 20 11.20 2 4 8.0 1 0 187.790 187.790 5.400 3.070 NA 36 32 NA 6.750 9.000 40.43 23.100 0.460 100.0 0 1 0 0 1 0 1 120 80 9 10 14.0 12.00 10.30
377 0 35 60.0 152.000 26.0 11 72 18 10.00 2 6 10.0 1 0 459.630 459.630 3.910 1.010 NA 38 35 NA 2.450 1.200 8.60 20.700 0.300 115.0 0 0 1 0 1 1 0 120 80 1 2 18.0 11.00 9.00
378 0 28 61.0 152.000 26.4 15 74 22 10.50 4 3 3.0 0 0 2.000 3.010 3.300 0.590 NA 39 35 NA 2.780 9.100 7.81 10.100 0.270 92.0 1 0 0 0 1 1 0 120 80 8 7 19.0 15.00 5.50
379 0 23 50.0 155.000 20.8 15 72 18 10.80 4 6 3.0 0 0 2.000 2.000 9.590 5.980 NA 37 34 NA 3.630 2.900 30.85 21.100 0.750 92.0 0 0 0 1 1 0 0 110 70 3 5 18.0 18.00 11.20
380 0 31 55.5 151.000 24.3 15 74 18 11.10 2 6 5.0 1 0 278.320 278.520 2.490 3.390 NA 38 34 NA 1.620 3.100 24.33 24.100 0.250 92.0 0 0 1 0 1 1 0 110 80 6 7 18.0 22.00 9.00
381 0 37 65.0 161.000 25.1 15 72 20 10.30 2 5 6.0 0 0 1.990 1.990 6.460 1.430 NA 38 34 NA 1.440 5.600 19.32 24.100 0.250 82.0 1 0 0 0 1 1 0 120 80 6 6 12.0 15.00 10.00
382 0 35 60.0 146.000 28.1 15 82 20 10.00 4 3 10.0 0 0 1.990 1.990 2.750 2.780 NA 37 35 NA 1.570 7.900 15.38 21.700 0.250 110.0 1 0 0 1 0 1 0 120 80 5 5 13.0 15.00 6.50
383 0 29 46.0 150.000 20.4 11 72 20 10.80 2 6 6.0 1 1 10.840 10.840 5.080 1.060 NA 34 33 NA 2.660 18.900 52.31 40.300 0.290 110.0 0 0 0 1 1 1 0 120 80 8 5 16.0 12.00 7.00
384 0 32 60.5 158.000 24.2 13 72 18 11.00 2 6 9.0 0 2 1.990 1.990 4.610 1.520 NA 38 34 NA 1.400 11.400 25.54 20.100 0.250 100.0 1 0 0 0 1 0 1 120 80 2 3 18.0 20.00 8.00
385 0 37 50.0 148.000 22.8 15 72 18 10.20 2 7 5.0 1 0 184.420 184.420 4.410 1.430 NA 37 33 NA 2.730 3.800 18.93 19.500 0.440 100.0 0 0 0 1 0 1 0 110 80 5 3 15.0 17.00 7.50
386 0 41 76.9 165.000 28.2 15 72 18 11.00 4 3 14.0 0 0 4.200 4.200 6.520 6.650 NA 42 38 NA 0.580 4.500 19.81 18.600 0.480 100.0 1 0 1 1 0 1 0 130 80 4 2 14.0 11.00 7.00
387 0 40 63.0 171.000 21.5 15 72 18 11.00 2 6 17.0 0 0 1.990 1.990 1.940 0.490 NA 38 34 NA 5.000 2.000 42.66 20.800 0.790 138.0 0 0 1 0 0 1 0 110 70 3 1 11.0 12.00 8.30
388 1 24 60.0 152.000 26.0 11 74 16 11.00 2 5 8.0 1 0 305.760 1.990 5.970 2.730 NA 38 32 NA 3.300 4.500 27.96 19.300 0.250 125.0 1 0 1 1 1 1 0 110 70 16 14 12.0 15.00 7.50
389 0 31 55.5 151.000 24.3 15 74 18 11.10 2 5 5.0 1 0 278.320 278.320 2.490 3.390 NA 37 34 NA 1.620 3.100 24.33 24.100 0.250 92.0 0 0 0 0 1 1 1 110 80 6 7 18.0 12.00 9.00
390 0 23 50.0 155.000 20.8 15 72 18 10.80 4 2 3.0 0 0 2.000 2.000 9.590 5.980 NA 36 32 NA 3.630 2.900 30.85 21.100 0.750 92.0 0 0 0 1 0 0 1 110 70 3 5 15.0 12.00 11.20
391 1 28 61.0 152.000 26.4 15 74 20 10.50 4 2 3.0 0 0 2.000 3.010 3.300 0.590 NA 38 35 NA 2.780 9.100 7.81 10.100 0.270 92.0 1 0 0 1 1 1 0 120 80 10 9 19.0 15.00 10.00
392 0 35 60.0 152.000 26.0 11 72 18 10.00 2 6 10.0 1 0 459.630 459.630 3.910 1.010 NA 39 36 NA 2.450 1.200 8.60 20.700 0.300 115.0 1 0 0 1 0 0 0 120 80 1 2 15.0 11.00 9.00
393 1 27 45.0 158.000 18.0 14 72 20 11.20 2 5 8.0 1 0 187.790 187.790 5.400 3.070 NA 36 32 NA 6.750 9.000 40.43 23.100 0.460 100.0 0 0 1 0 0 1 1 120 80 10 11 15.0 13.00 10.30
394 0 32 65.0 161.000 25.1 11 74 20 10.20 2 5 11.0 1 0 6.190 6.190 2.170 2.020 NA 38 35 NA 4.780 0.200 99.93 17.300 0.250 115.0 1 0 0 0 0 1 0 110 70 1 2 13.0 15.00 7.50
395 0 28 42.0 163.000 15.8 13 70 18 12.00 2 6 7.0 1 0 347.980 1.990 2.590 0.630 NA 35 30 NA 5.740 5.800 33.04 39.300 0.395 84.0 0 0 1 0 1 1 0 120 70 8 7 16.0 15.00 11.00
396 1 29 74.0 159.000 29.3 11 72 20 10.80 4 3 3.0 1 0 346.590 346.590 5.760 6.420 NA 42 38 NA 0.950 6.000 45.54 36.200 0.320 92.0 1 0 1 1 1 1 0 120 80 10 9 15.0 14.00 8.00
397 0 31 68.0 161.000 26.2 15 72 18 12.80 2 6 6.0 1 0 96.220 5.390 1.580 0.240 NA 40 37 NA 6.910 5.100 15.85 25.600 0.500 87.0 1 0 0 1 1 0 0 110 80 4 9 12.0 15.00 7.00
398 0 35 60.0 152.000 26.0 15 74 18 10.00 2 6 3.0 1 0 116.310 116.310 6.520 3.420 NA 38 34 NA 0.770 2.500 40.73 20.000 0.300 92.0 1 0 0 0 1 0 1 110 80 3 4 12.0 11.00 8.00
399 1 36 60.0 154.000 25.3 11 72 18 11.20 2 4 5.0 1 1 232.640 232.640 3.720 1.600 NA 38 35 NA 1.600 1.600 18.35 24.600 0.480 92.0 1 1 0 0 1 1 0 110 80 6 9 19.0 17.00 5.80
400 0 30 67.0 165.000 24.6 13 80 20 10.00 2 6 5.0 1 0 167.410 167.410 5.730 2.420 NA 39 35 NA 2.310 1.900 25.13 12.400 0.580 100.0 0 0 0 1 0 1 0 120 70 3 3 18.0 19.00 9.60
401 0 27 58.0 161.000 22.4 13 74 18 10.50 2 5 8.0 0 0 1.990 1.990 2.100 1.080 NA 37 34 NA 3.400 1.140 10.76 25.400 0.250 100.0 0 0 0 1 1 0 0 120 70 4 4 14.0 16.00 7.00
402 0 28 65.6 159.000 25.9 11 72 20 10.50 2 6 5.0 0 0 2.000 2.000 4.440 1.030 NA 39 36 NA 3.670 5.700 23.65 15.500 0.580 130.0 1 0 0 1 0 1 0 120 80 5 5 14.0 16.00 8.00
403 1 28 65.0 158.000 25.0 11 74 20 10.50 4 3 4.5 1 0 230.530 230.530 5.180 6.020 NA 36 32 NA 1.570 28.600 12.62 21.200 0.250 125.0 1 0 1 1 1 1 0 110 70 11 12 19.0 17.00 11.00
404 1 27 64.0 160.000 25.0 13 78 22 10.80 2 5 3.0 0 0 2.000 2.000 3.070 0.550 NA 36 32 NA 2.280 5.500 28.89 25.100 0.250 80.0 1 0 1 1 0 1 0 120 80 4 11 18.0 20.00 7.00
405 0 26 38.0 148.000 17.3 15 72 18 10.50 2 6 3.0 1 0 10.400 10.400 2.720 2.530 NA 28 25 NA 1.500 3.800 42.01 47.800 0.460 130.0 0 0 1 0 0 0 0 120 70 4 5 18.0 20.00 9.50
406 0 33 50.0 158.000 20.0 15 74 18 10.20 2 5 2.0 1 0 465.010 465.010 3.390 2.770 NA 35 31 NA 2.770 4.200 19.06 43.300 0.310 82.0 0 0 0 1 1 1 0 120 80 2 3 14.0 24.00 6.80
407 0 33 45.0 142.000 22.3 11 72 20 11.50 2 6 9.0 0 0 1.990 1.990 2.390 0.890 NA 35 31 NA 1.670 0.500 38.45 23.700 0.280 85.0 0 0 0 1 0 1 0 120 80 1 3 17.0 20.00 9.00
408 0 36 59.0 155.000 24.6 15 74 20 10.00 2 5 17.0 0 0 2.000 2.000 5.210 1.780 NA 38 34 NA 1.870 0.900 22.83 25.900 0.380 100.0 0 0 0 0 1 1 0 120 80 2 3 17.0 18.00 11.00
409 0 45 57.0 160.000 22.3 15 72 22 10.30 2 6 23.0 0 0 1.990 1.990 4.080 3.600 NA 38 35 NA 1.960 4.800 20.19 17.400 0.400 130.0 0 0 1 0 1 1 0 110 70 3 6 14.0 12.00 9.00
410 0 40 60.0 161.000 23.1 13 74 20 11.20 2 6 13.0 1 0 41.750 41.750 5.130 2.970 NA 38 35 NA 2.780 1.300 11.81 16.100 0.250 146.0 0 0 0 1 1 0 0 110 70 3 0 10.0 11.00 4.00
411 0 34 54.7 158.000 21.9 13 72 20 10.40 2 5 9.0 1 0 363.080 8.540 6.710 8.710 NA 37 33 NA 3.650 6.400 12.31 25.800 4.250 100.0 0 0 0 1 1 1 1 120 80 1 1 13.0 15.00 8.00
412 0 40 53.0 150.000 23.6 15 78 20 11.40 2 5 14.0 1 0 382.360 382.360 4.800 3.830 NA 34 30 NA 1.980 8.100 8.10 44.100 0.250 115.0 0 0 0 1 0 1 0 120 70 3 4 2.0 13.00 11.00
413 1 23 55.0 152.000 23.2 13 78 18 10.80 2 5 3.0 0 0 1.990 1.990 40.080 1.990 NA 36 32 NA 1.350 10.800 43.94 29.400 0.400 108.0 0 1 1 1 1 0 1 110 70 10 18 14.0 14.00 10.30
414 0 26 52.0 150.000 23.1 15 74 20 10.00 2 5 9.0 0 0 1.990 1.990 3.520 2.140 NA 35 31 NA 0.740 5.200 15.59 30.100 0.300 115.0 0 0 0 1 1 1 0 110 70 1 1 13.0 14.00 13.00
415 1 30 64.0 158.000 25.6 11 74 22 10.20 2 4 2.2 1 0 2229.810 10.980 4.750 7.200 NA 38 35 NA 4.750 7.200 32.06 33.800 0.250 92.0 1 0 1 0 1 1 0 110 70 12 14 14.0 14.00 9.50
416 1 31 60.0 160.000 23.5 15 80 20 10.80 4 2 7.0 1 0 252.720 252.720 1.800 0.900 NA 39 35 NA 1.860 15.000 41.94 23.600 0.250 92.0 0 1 1 0 1 1 0 120 80 8 12 12.0 16.00 6.50
417 0 26 45.0 149.000 20.3 13 74 20 10.20 2 5 8.0 1 0 67.200 67.200 7.140 3.790 NA 32 28 NA 1.000 3.400 10.80 28.100 0.250 100.0 0 0 0 1 0 1 0 120 80 6 2 14.0 13.00 8.00
418 0 32 71.8 162.000 27.4 11 72 18 10.80 2 6 4.0 0 0 1.990 1.990 3.680 0.130 NA 41 38 NA 2.640 2.280 20.93 27.100 0.250 92.0 1 0 1 0 1 0 1 120 80 3 3 14.0 13.00 9.00
419 1 27 65.0 160.000 25.4 15 74 20 11.20 2 4 2.0 0 0 1.990 1.990 3.850 1.020 NA 37 34 NA 2.390 2.600 9.34 15.600 0.250 110.0 1 0 1 0 1 1 0 110 80 4 9 10.0 15.00 6.70
420 0 36 58.0 154.000 24.5 15 72 18 11.00 2 5 12.0 0 0 1.990 1.990 4.750 2.460 NA 36 32 NA 0.950 4.800 26.39 25.400 0.350 92.0 0 0 0 1 1 1 0 110 80 3 3 13.0 15.00 7.80
421 1 25 45.6 152.000 19.7 11 74 20 11.00 2 5 1.0 1 0 6.921 6.921 5.120 3.600 NA 30 26 NA 1.880 11.900 20.52 29.300 0.250 115.0 0 1 0 1 1 1 0 120 80 14 10 15.0 15.00 10.00
422 0 40 74.1 164.000 27.6 15 78 20 11.50 2 6 18.0 0 0 1.990 1.990 1.000 0.600 NA 42 38 NA 4.450 6.900 26.35 17.100 0.350 92.0 1 0 1 0 1 1 1 120 80 2 4 10.0 16.00 7.20
423 1 38 68.0 164.000 25.3 15 74 20 10.40 4 2 1.0 1 0 482.210 482.210 7.100 4.270 NA 38 35 NA 3.980 0.100 6.18 25.300 0.370 107.0 1 0 1 1 1 1 0 120 80 13 12 15.0 15.00 8.20
424 0 42 55.0 150.000 24.4 13 74 20 10.70 2 5 5.0 1 0 40.220 1.990 2.970 0.200 NA 36 32 NA 3.580 12.800 37.90 23.100 0.300 107.0 0 0 0 1 1 1 0 120 80 4 5 14.0 10.00 11.00
425 0 29 51.0 158.000 20.4 15 72 20 11.00 2 5 7.0 1 0 50.110 0.990 2.010 1.810 NA 32 28 NA 1.340 2.700 71.77 23.500 0.250 100.0 0 0 0 0 0 1 1 110 80 6 3 12.0 11.00 8.50
426 1 37 59.0 148.000 26.9 11 74 20 10.80 4 3 4.0 0 0 1.990 1.990 7.610 4.550 NA 36 32 NA 3.510 6.000 13.62 26.400 0.290 92.0 1 0 1 0 1 1 0 120 80 8 12 17.0 0.17 11.20
427 0 33 64.0 158.000 25.6 13 74 20 11.00 2 5 12.0 1 1 103.500 103.500 3.350 0.560 NA 36 32 NA 2.980 0.840 25.97 23.800 0.250 106.0 1 0 0 1 0 0 1 110 80 7 3 19.0 17.00 9.00
428 1 32 63.0 158.000 25.2 11 72 18 10.60 4 2 10.0 1 1 102.870 102.870 3.270 2.300 NA 36 31 NA 0.860 2.900 67.97 18.100 0.380 115.0 1 0 1 1 1 0 0 110 70 3 11 19.0 17.00 8.90
429 1 28 48.0 148.000 21.9 11 74 20 10.20 2 6 4.0 1 0 90.670 90.670 4.000 1.610 NA 30 26 NA 1.150 4.600 64.47 34.400 0.250 100.0 0 0 1 1 1 1 0 110 70 11 9 19.0 15.00 11.50
430 1 33 60.0 152.000 26.0 13 72 18 12.50 2 5 6.0 1 0 375.180 375.180 2.500 1.170 NA 38 34 NA 2.530 20.000 25.37 35.100 0.250 92.0 1 0 1 1 1 1 0 130 80 14 16 16.0 16.00 10.20
431 1 25 65.0 154.000 27.4 13 72 20 11.00 4 3 5.0 1 0 728.010 728.010 4.590 2.340 NA 36 32 NA 2.380 8.900 24.84 18.700 0.460 72.0 1 0 1 0 0 0 1 120 80 4 6 13.0 14.00 7.60
432 0 42 65.0 158.000 26.0 11 72 18 10.50 2 6 2.0 1 0 433.970 433.970 6.760 1.760 NA 38 35 NA 3.505 1.200 12.40 22.700 0.350 115.0 1 0 0 0 1 1 0 110 70 5 7 14.0 15.00 7.00
433 0 29 50.0 148.000 22.8 15 72 22 11.00 2 5 5.0 0 0 1.990 1.990 5.640 2.710 NA 32 28 NA 1.240 2.800 15.31 32.700 0.250 225.0 0 0 0 1 1 0 0 120 70 4 6 10.0 13.00 7.00
434 0 33 57.0 163.000 21.5 15 72 18 11.50 2 6 10.0 0 0 1.990 1.990 4.090 2.630 NA 32 28 NA 1.080 10.300 10.62 27.200 0.250 100.0 0 0 0 1 1 1 1 110 70 5 7 13.0 11.00 10.00
435 1 23 70.0 158.000 28.0 13 72 18 11.00 2 5 2.0 0 0 1.990 1.990 4.500 1.500 NA 42 38 NA 2.040 9.900 17.50 18.000 0.500 92.0 1 0 1 1 1 1 0 110 70 8 11 14.0 15.00 11.00
436 0 30 64.0 170.000 22.1 15 72 18 11.10 2 5 6.0 0 0 1399.000 1.990 3.750 1.570 NA 40 36 NA 1.510 2.500 27.72 31.800 0.250 94.0 0 0 0 1 1 0 1 110 80 4 3 11.0 16.00 9.00
437 0 29 48.0 150.000 21.3 15 70 18 10.20 2 6 6.0 1 0 213.140 213.140 13.900 0.500 NA 32 27 NA 0.820 3.600 30.98 74.500 0.800 92.0 0 0 0 1 0 1 0 110 80 4 4 14.0 13.00 10.00
438 1 32 71.0 157.000 28.8 13 74 20 10.50 4 3 13.0 0 0 4.200 4.200 6.550 4.300 NA 42 38 NA 2.270 8.000 18.94 36.600 0.250 85.0 1 0 1 1 1 1 0 120 80 9 12 18.0 19.00 8.00
439 1 33 76.0 164.000 28.3 11 72 20 11.00 4 2 6.0 1 1 86.420 238.140 3.470 3.130 NA 42 39 NA 3.900 1.010 30.53 32.900 0.380 100.0 1 0 1 0 1 1 0 110 80 11 9 16.0 19.00 10.00
440 1 34 73.0 161.000 28.2 13 72 20 11.20 2 5 3.0 1 0 70.530 1.990 4.540 1.360 NA 40 36 NA 2.330 10.200 15.26 18.200 0.250 130.0 1 0 0 1 1 1 0 110 80 7 13 15.0 19.00 10.30
441 0 26 56.0 160.000 20.3 11 70 20 11.10 2 6 3.0 1 0 1.990 181.230 5.710 2.420 NA 34 30 NA 1.460 11.000 21.87 16.900 0.250 110.0 0 0 0 1 0 1 1 110 70 9 6 19.0 14.00 8.00
442 0 31 64.0 170.000 22.1 15 74 18 11.00 4 3 6.0 0 0 3.350 1.990 1.060 1.490 NA 36 32 NA 3.020 0.600 10.43 12.000 0.860 115.0 0 0 0 0 1 0 1 110 80 3 5 11.0 12.00 3.00
443 0 40 56.0 158.000 22.4 15 72 18 12.80 2 6 22.0 1 2 407.250 491.850 5.650 3.310 NA 34 30 NA 3.850 0.500 29.50 24.300 0.540 107.0 0 0 0 1 0 0 1 110 70 6 2 9.0 11.00 7.00
444 1 39 55.0 150.000 24.4 15 78 20 10.70 4 7 13.0 1 0 12.370 19.440 5.400 1.950 NA 38 36 NA 1.000 0.370 21.88 18.600 0.460 94.0 0 0 0 0 1 1 0 120 80 12 11 19.0 19.00 8.10
445 1 27 52.0 150.000 23.1 11 82 20 11.10 2 5 5.0 0 0 1.990 1.990 4.970 2.230 NA 36 32 NA 1.940 16.900 9.12 23.600 0.250 93.0 0 0 0 0 0 1 1 120 80 1 1 16.0 17.00 8.55
446 1 26 72.0 150.000 32.0 15 72 20 12.00 4 2 3.0 0 0 144.630 1.990 3.630 8.340 NA 42 40 NA 2.920 26.400 29.72 22.400 0.380 93.0 1 0 1 0 1 1 0 110 80 8 7 20.0 20.00 6.00
447 0 29 63.0 162.000 24.0 11 72 18 13.20 2 5 11.0 0 0 30004.000 475.040 1.990 1.610 NA 39 37 NA 1.770 5.960 21.95 22.700 0.460 95.0 0 1 1 0 0 1 0 110 80 6 7 18.0 19.00 5.90
448 1 47 62.7 160.000 24.5 15 72 18 10.00 4 7 30.0 0 0 30007.000 1.990 1.880 0.250 NA 39 36 NA 2.470 6.200 31.47 12.100 0.250 92.0 0 0 0 0 1 1 1 110 80 14 11 20.0 19.00 6.00
449 0 41 71.5 160.000 27.9 15 74 20 11.00 4 7 16.0 1 1 1.900 1.990 4.740 3.600 NA 42 39 NA 1.090 9.100 22.62 31.600 0.250 85.0 1 1 1 0 0 1 0 120 80 9 7 19.0 18.00 8.00
450 1 33 63.0 159.000 24.9 15 72 18 10.10 4 2 7.0 1 0 294.060 1.990 5.020 12.040 NA 39 37 NA 3.150 0.800 33.23 40.200 0.300 100.0 0 0 0 0 1 1 0 120 80 6 8 19.0 17.00 8.20
451 1 34 69.0 167.640 24.6 15 80 20 11.50 2 5 10.0 0 0 53.510 13.120 4.070 1.930 NA 40 38 NA 3.080 0.910 25.61 61.300 0.320 107.0 1 1 0 1 1 1 0 110 80 20 16 18.0 14.00 7.00
452 1 30 44.0 161.544 16.9 12 72 18 11.40 2 4 4.0 1 0 595.800 1.920 8.250 0.460 NA 28 24 NA 3.510 0.980 26.42 30.500 0.240 84.0 0 0 0 0 0 0 1 100 70 5 18 11.0 21.00 9.65
453 1 31 50.0 158.000 20.0 11 74 20 10.80 2 5 11.0 0 0 482.870 1.990 3.070 0.500 NA 36 30 NA 1.330 0.890 19.51 29.200 0.250 92.0 0 0 1 0 1 1 1 110 80 10 19 17.0 18.00 8.50
454 1 35 57.0 154.000 24.0 15 72 18 10.70 2 5 7.0 0 0 950.410 1.990 4.410 1.970 NA 38 32 NA 5.000 0.870 12.71 16.000 0.270 92.0 0 0 0 0 0 0 1 110 70 11 15 19.0 21.00 7.85
455 1 35 61.0 158.000 24.4 13 72 18 10.50 2 5 13.0 0 1 355.510 1.990 3.710 2.670 NA 38 33 NA 2.970 9.700 20.35 15.000 0.290 93.0 0 0 1 0 1 1 1 120 80 12 13 19.0 18.00 8.20
456 1 32 52.0 150.000 23.1 13 70 18 9.40 2 5 12.0 0 0 272.780 1.990 4.330 2018.000 NA 36 32 NA 2.100 7.700 40.47 41.040 0.250 93.0 0 0 0 0 1 0 1 110 80 6 7 18.0 18.00 9.00
457 1 31 51.0 156.000 21.0 17 78 20 11.10 2 6 5.0 0 0 148.520 1.990 4.960 1.750 NA 36 31 NA 2.200 1.100 12.11 25.700 0.350 93.0 0 0 1 0 1 0 1 120 70 14 10 17.0 17.00 7.00
458 1 31 38.0 158.496 15.1 15 80 20 12.00 4 2 3.0 0 0 1.920 1.990 3.990 6.630 NA 26 25 NA 1.300 17.600 13.91 19.500 0.250 133.0 0 0 0 0 1 0 0 120 70 7 6 17.0 19.00 8.00
459 1 36 66.0 162.000 25.1 15 72 20 11.00 4 7 NA 0 0 515.330 1.990 1.640 0.170 NA 39 37 NA 1.860 6.600 25.00 20.800 0.250 93.0 0 0 0 0 0 0 0 120 80 14 5 19.0 19.00 8.00
460 0 36 55.0 154.000 23.2 13 74 18 10.00 2 5 8.0 0 0 8.000 1.990 3.220 0.100 NA 38 37 NA 2.910 5.000 22.84 53.210 0.260 92.0 0 0 0 1 0 1 0 110 80 4 1 22.0 14.00 10.50
461 0 27 63.0 158.000 25.2 12 72 18 11.20 2 4 2.5 0 1 152.670 1.990 7.900 3.360 NA 39 37 NA 2.280 7.000 13.11 42.120 0.250 80.0 0 0 0 0 0 1 0 110 70 5 8 18.0 19.00 7.35
462 0 33 55.0 158.496 21.9 15 72 22 12.00 2 5 6.0 1 0 480.200 1.900 3.820 1.380 NA 38 36 NA 2.160 9.000 32.37 19.800 0.240 92.0 0 0 0 0 0 1 0 110 80 0 4 0.0 20.00 8.90
463 1 33 89.0 163.000 33.5 12 78 22 10.80 4 7 6.0 1 0 381.990 15.000 3.450 0.720 NA 46 40 NA 0.730 0.700 15.23 16.700 0.280 100.0 1 0 1 1 1 1 0 120 80 13 12 19.0 18.00 7.90
464 0 41 45.0 160.000 17.6 14 78 22 11.00 2 5 18.0 1 0 152.900 268.370 4.410 1.270 NA 38 37 NA 2.190 10.000 22.61 25.456 0.300 100.0 0 1 1 1 1 1 0 110 80 2 1 19.0 18.00 7.20
465 1 34 54.0 153.000 23.1 16 72 20 11.00 4 7 3.5 0 0 598.080 1.990 2.240 2.300 NA 36 35 NA 6.660 0.850 60.40 46.210 0.250 90.0 0 0 0 0 0 0 0 110 80 21 19 16.0 18.00 7.60
466 0 33 50.0 150.000 22.2 15 70 18 10.80 2 4 8.0 0 0 194.390 1.990 2.580 0.200 NA 36 34 NA 1.790 6.000 20.83 58.320 0.450 90.0 0 0 0 0 0 0 0 110 80 6 9 20.0 18.00 8.00
467 0 35 61.8 153.000 26.4 17 70 20 12.00 2 4 11.0 0 0 1.990 1.990 6.750 3.230 NA 39 38 NA 2.280 6.000 10.61 62.500 0.480 100.0 0 0 0 0 0 1 0 120 80 11 9 21.0 16.00 8.00
468 1 32 64.0 162.000 24.4 12 72 18 11.50 4 2 9.0 1 0 561.720 1.990 3.580 3.550 NA 39 34 NA 0.580 0.780 25.37 15.300 0.360 102.0 1 0 0 0 0 0 0 110 70 13 12 19.0 19.00 7.00
469 0 40 56.0 148.000 25.6 12 72 18 10.50 2 5 7.0 1 1 10.000 1.990 6.560 3.440 NA 38 34 NA 5.000 16.000 32.46 21.600 0.250 92.0 0 0 1 0 0 1 1 110 80 9 10 22.0 17.00 8.15
470 1 25 55.0 154.000 23.2 15 74 20 12.00 2 5 4.0 0 0 10.000 1.990 4.670 0.710 NA 38 36 NA 1.820 0.740 24.81 60.265 0.350 102.0 0 0 0 1 0 0 1 110 70 12 12 24.0 18.00 8.90
471 1 34 52.0 149.000 23.4 13 80 20 12.00 4 7 10.0 0 2 1.980 8.000 5.480 6.370 NA 36 32 NA 2.930 0.910 19.21 12.300 0.340 100.0 0 1 1 1 0 1 1 110 80 8 10 17.0 20.00 7.00
472 1 31 50.0 152.000 21.6 13 80 20 11.00 2 5 7.0 0 0 381.900 1.990 6.210 1.000 NA 36 34 NA 1.750 0.990 32.06 18.500 0.250 90.0 0 0 0 1 0 1 0 110 70 5 14 15.0 20.00 7.50
473 1 26 68.0 164.592 25.1 11 72 18 12.00 2 5 6.0 1 1 10.000 9.350 5.050 2.010 NA 42 38 NA 1.270 0.900 14.85 32.800 0.240 92.0 1 0 0 0 1 1 0 110 70 12 14 19.0 18.00 8.95
474 0 39 50.0 154.000 21.1 15 78 22 12.10 2 5 16.0 0 0 1.970 1.990 3.800 2.000 NA 34 30 NA 4.350 4.130 14.13 23.700 0.250 100.0 0 0 0 0 0 0 0 120 80 5 7 19.0 20.00 5.00
475 0 48 55.0 151.000 24.1 15 70 18 9.40 4 2 25.0 0 0 4983.210 127.200 4.080 2.600 NA 38 36 NA 0.450 4.020 27.72 29.200 0.300 91.0 0 0 0 0 1 0 1 130 80 9 10 18.0 19.00 9.00
476 1 31 55.0 156.000 22.6 13 72 20 12.10 2 4 7.0 1 0 1.990 56.400 6.530 7.480 NA 38 32 NA 2.310 6.090 14.79 30.800 0.250 84.0 0 0 1 0 0 0 1 120 80 6 15 18.0 20.00 8.50
477 0 32 61.0 165.000 22.4 17 78 20 11.50 2 5 1.5 1 0 1.970 1.990 9.140 3.370 NA 40 35 NA 0.580 0.710 20.07 36.300 0.640 116.0 0 0 0 0 0 0 1 110 80 2 1 20.0 17.00 6.40
478 1 42 94.0 155.448 38.9 16 74 18 10.70 4 7 16.0 0 0 501.310 1.990 5.590 2.020 NA 48 47 NA 1.910 3.620 7.38 28.200 0.550 91.0 1 1 1 1 0 1 0 130 80 18 11 20.0 19.00 9.20
479 0 28 60.0 160.000 23.4 15 72 18 11.70 2 5 4.0 0 0 21.450 168.990 8.250 2.840 NA 39 37 NA 1.000 0.890 33.24 26.300 0.390 91.0 0 0 0 0 0 0 0 120 80 3 5 18.0 17.00 6.00
480 0 27 60.0 158.496 23.9 11 72 20 10.40 2 5 7.0 1 0 696.830 1.990 5.860 2.370 NA 39 38 NA 1.000 1.970 15.82 25.300 0.400 116.0 0 0 1 1 1 0 0 110 80 5 8 18.0 18.00 9.00
481 0 26 52.0 164.592 19.2 13 70 18 10.00 2 5 3.0 0 0 22.000 1.990 6.800 0.430 NA 36 35 NA 1.470 1.400 49.01 24.500 0.250 84.0 0 0 0 0 0 0 0 110 80 7 5 20.0 21.00 8.00
482 0 44 66.1 148.000 30.8 13 70 20 10.40 2 5 8.0 0 0 1.890 1.990 7.810 2.020 NA 40 34 NA 3.250 3.030 13.79 27.500 0.400 125.0 1 0 0 0 1 0 0 130 80 5 9 16.0 19.00 4.50
483 0 32 43.0 161.544 16.5 13 72 22 12.10 2 5 7.0 1 0 1.990 1.990 5.510 4.100 NA 34 30 NA 2.270 3.860 40.29 32.600 0.370 108.0 0 0 0 0 1 1 1 110 80 1 3 19.0 19.00 8.60
484 1 33 87.9 158.000 35.2 15 72 20 10.00 4 2 8.0 0 0 70.350 1.990 2.810 0.370 NA 46 44 NA 6.550 5.750 28.05 21.000 0.210 116.0 1 0 0 0 1 1 0 120 80 7 12 20.0 14.00 8.00
485 0 31 63.0 156.000 25.9 11 72 20 10.80 2 5 5.0 0 0 1.990 1.990 5.280 3.460 NA 39 36 NA 2.480 6.260 28.08 16.250 25.300 116.0 0 1 0 1 0 1 1 120 80 9 10 18.0 17.00 7.25
486 0 30 44.8 157.000 18.2 15 80 20 10.00 2 5 9.0 1 0 1238.400 237.500 1.000 0.100 NA 34 30 NA 3.260 0.720 44.42 33.300 0.250 91.0 0 0 0 0 0 1 1 110 80 2 4 15.0 18.00 8.50
487 1 30 49.1 159.000 19.4 11 74 18 11.70 4 2 4.0 1 0 710.400 1.990 7.590 6.690 NA 36 32 NA 3.580 10.530 22.73 21.700 0.390 116.0 0 1 1 0 0 1 1 110 80 16 16 19.0 19.00 10.50
488 0 36 62.0 152.000 26.8 15 74 20 12.10 4 2 9.0 0 3 1.970 1.990 3.350 5.060 NA 40 37 NA 1.750 10.320 24.63 27.600 0.340 84.0 1 1 0 0 0 1 0 120 70 4 4 18.0 19.00 6.75
489 0 36 76.9 165.000 28.2 11 72 18 10.80 2 5 7.0 0 0 37.190 1.990 5.650 2.010 NA 42 41 NA 6.960 2.390 16.64 22.000 0.250 116.0 1 0 0 0 0 1 0 110 80 5 9 20.0 19.00 6.80
490 1 28 63.0 143.000 30.8 11 72 18 11.80 2 4 11.0 0 0 1.990 1.990 7.090 2.340 NA 40 39 NA 0.270 2.700 44.36 37.600 0.370 84.0 1 0 0 0 0 0 0 120 80 7 15 16.0 16.00 10.00
491 0 35 61.0 154.000 25.7 12 80 20 10.20 4 7 16.0 0 0 1.990 1.990 4.620 1.230 NA 40 38 NA 1.170 1.100 54.86 27.200 0.500 92.0 1 1 1 1 1 1 0 120 70 3 2 19.0 16.00 6.00
492 0 23 66.0 164.592 24.4 16 72 18 11.50 4 2 3.0 1 0 658.290 1.990 8.130 1.390 NA 38 37 NA 4.140 4.900 27.20 36.100 0.250 100.0 0 0 0 0 1 1 0 110 80 6 5 18.0 20.00 7.20
493 0 25 58.0 155.448 24.0 14 72 18 10.80 2 5 3.0 1 0 1.990 1.990 5.680 4.210 NA 34 32 NA 2.760 10.600 24.01 19.300 0.480 92.0 0 1 0 1 0 0 0 120 80 1 2 19.0 18.00 10.35
494 1 30 50.0 154.000 21.1 15 72 20 10.80 2 4 6.0 1 0 492.020 1.990 7.730 0.260 NA 34 33 NA 8.300 4.300 13.14 25.700 0.250 85.0 0 0 0 0 0 0 1 120 80 9 19 19.0 18.00 6.00
495 0 30 48.0 148.000 21.9 15 80 20 10.50 2 5 6.0 1 0 341.470 1.990 2.440 0.350 NA 28 27 NA 1.470 5.100 13.38 38.200 0.251 92.0 1 0 1 0 1 1 0 110 70 5 8 18.0 18.00 7.00
496 1 27 64.0 155.000 26.6 13 72 20 11.50 4 2 5.0 0 0 275.600 1.990 5.740 1.290 NA 40 36 NA 1.510 16.800 27.51 23.400 0.610 140.0 1 1 1 1 1 1 0 120 70 10 11 18.0 16.00 6.40
497 0 34 45.0 150.000 20.0 17 70 18 10.70 2 5 8.0 0 0 336.510 1.990 3.100 3.480 NA 28 26 NA 2.170 7.900 14.08 27.100 0.400 103.0 0 0 0 0 0 0 0 110 80 5 4 18.0 16.00 5.40
498 0 35 54.0 140.000 27.6 11 72 20 10.80 2 5 5.0 1 0 5.650 1.990 4.620 1.330 NA 36 34 NA 4.070 3.100 27.17 31.600 0.380 112.0 0 0 0 0 1 0 0 110 70 9 7 20.0 16.00 6.60
499 0 23 52.0 148.000 23.7 15 72 20 11.00 2 5 4.0 0 0 1.990 1.990 7.720 4.100 NA 32 30 NA 5.000 5.100 12.60 25.900 0.500 92.0 0 0 0 0 0 0 0 120 80 1 1 7.0 17.00 6.00
500 0 28 65.0 163.000 24.5 14 74 20 10.80 2 5 3.0 0 0 296.570 1.990 6.500 10.500 NA 32 31 NA 1.830 11.200 34.16 33.000 0.300 100.0 0 0 0 0 0 0 0 120 80 8 7 18.0 20.00 8.80
501 0 40 59.0 152.000 25.5 13 72 18 11.50 2 5 10.0 1 1 1.990 1.990 3.870 1.070 NA 44 42 NA 2.920 1.700 7.12 34.200 0.490 120.0 1 1 0 1 0 0 0 100 70 3 5 17.0 18.00 14.20
502 0 38 70.0 164.592 25.8 16 72 18 12.10 2 4 8.0 0 0 1.990 1.990 3.500 0.200 NA 46 42 NA 3.100 5.200 24.68 32.200 0.450 92.0 1 1 0 0 1 0 0 110 70 2 7 19.0 19.00 9.00
503 0 38 52.0 150.000 23.1 15 80 20 11.00 2 5 15.0 0 0 469.550 1.990 6.810 5.750 NA 36 35 NA 1.220 2.200 37.56 25.300 0.250 100.0 0 0 0 0 0 0 0 120 80 3 1 21.0 19.00 9.30
504 0 31 52.9 153.000 22.6 16 70 20 11.00 2 5 9.0 0 0 147.900 1.990 7.160 4.210 NA 34 32 NA 4.520 1.300 23.29 15.100 0.350 85.0 0 0 0 0 0 0 0 120 80 3 3 17.0 18.00 6.80
505 0 33 48.0 162.000 18.3 17 72 18 10.20 2 5 5.0 0 0 6.000 10.240 7.050 4.510 NA 28 24 NA 8.470 12.000 22.29 28.600 0.220 100.0 0 0 0 0 0 0 0 110 80 7 10 18.0 16.00 10.00
506 0 35 52.0 152.000 22.5 13 74 20 11.10 2 4 15.0 1 1 1.990 1.990 2.200 0.510 NA 30 29 NA 2.460 3.300 40.41 24.000 0.310 115.0 0 0 0 0 0 0 0 110 70 3 3 18.0 18.00 7.00
507 0 35 42.0 152.400 18.1 11 78 18 10.80 2 5 12.0 0 1 300.530 1.990 1.680 0.300 NA 28 24 NA 3.070 1.700 32.87 15.000 0.490 108.0 0 0 1 1 0 0 0 120 80 3 10 16.0 21.00 7.00
508 0 37 62.0 153.000 26.5 11 74 20 10.70 2 4 13.0 0 0 316.490 1.990 8.060 3.280 NA 44 43 NA 1.740 0.900 10.91 24.300 0.330 107.0 0 1 0 1 0 1 0 110 80 6 6 19.0 19.00 7.50
509 0 36 54.3 150.000 24.1 11 78 20 10.00 2 5 12.0 0 0 1.990 1.990 7.400 3.970 NA 32 30 NA 2.120 4.900 20.84 17.900 0.250 138.0 0 1 0 0 0 0 0 110 70 1 1 16.0 19.00 9.10
510 1 46 54.0 152.000 23.4 13 74 20 14.00 2 4 16.0 1 1 856.110 1.990 2.850 0.370 NA 30 28 NA 1.130 16.600 8.10 23.700 0.290 92.0 1 0 0 0 0 1 0 120 70 20 18 18.0 17.00 7.80
511 0 24 57.0 158.496 22.7 17 70 20 11.20 2 5 3.0 0 0 1.990 1.990 1.720 0.430 NA 32 30 NA 4.020 17.600 25.99 44.800 0.700 100.0 1 0 0 0 0 0 0 120 70 8 10 18.0 19.00 8.40
512 0 44 56.0 142.000 27.8 13 72 18 11.80 2 5 3.0 0 0 42.610 1.990 2.810 0.150 NA 38 36 NA 1.010 4.600 10.89 16.900 0.690 82.0 0 0 0 0 1 0 0 110 80 9 5 19.0 19.00 6.00
513 1 23 72.0 165.000 26.4 13 72 18 10.60 5 7 5.0 0 0 121.050 1.990 2.130 0.570 NA 42 40 NA 1.000 5.500 16.33 17.000 0.250 92.0 0 1 1 0 0 1 0 120 70 8 10 16.0 18.00 9.30
514 0 21 55.0 158.496 21.9 16 72 18 11.50 2 5 3.0 0 0 1.990 1.990 8.550 2.530 NA 40 38 NA 1.790 2.200 17.23 13.800 0.230 100.0 0 0 0 0 0 0 0 120 80 1 3 7.0 19.00 8.50
515 0 32 65.0 170.688 22.3 16 72 18 10.50 2 5 6.0 0 0 171.850 1.990 7.450 2.860 NA 38 36 NA 2.970 18.900 21.04 30.000 0.490 100.0 0 0 0 0 0 1 0 110 70 9 7 18.0 18.00 9.00
516 0 31 69.0 164.000 25.7 15 72 18 11.00 2 5 4.0 0 0 1.990 1.990 6.540 2.310 NA 46 44 NA 1.410 10.000 19.19 22.900 0.300 92.0 0 0 0 1 0 0 0 110 80 10 8 20.0 17.00 7.00
517 0 34 50.0 152.000 21.6 15 72 18 10.00 2 5 8.0 0 0 1.990 104.870 3.230 2.700 NA 32 28 NA 3.920 16.000 7.16 21.700 0.250 92.0 0 0 1 0 0 0 0 100 70 12 10 19.0 16.00 9.20
518 0 27 59.0 152.000 25.2 11 72 18 11.50 2 5 2.0 0 0 320.070 1.990 5.950 3.900 NA 36 34 NA 2.210 5.400 35.19 24.200 0.500 92.0 0 0 0 0 0 0 0 110 70 5 8 20.0 21.00 9.80
519 0 24 56.0 154.000 23.6 17 72 18 10.50 2 5 4.0 0 0 31.770 1.990 7.700 3.340 NA 30 28 NA 3.850 3.700 27.37 19.700 0.400 92.0 0 0 0 0 0 0 0 110 70 5 4 19.0 20.00 5.90
520 0 28 53.0 158.496 21.1 16 72 18 11.80 2 5 5.0 1 0 1.990 1.990 5.570 3.750 NA 28 26 NA 2.080 5.500 23.01 32.000 0.390 100.0 0 0 1 0 0 0 0 110 80 4 4 19.0 16.00 6.50
521 1 27 50.0 168.000 17.7 13 72 18 12.00 4 2 7.0 0 0 632.220 1.990 4.560 2.160 NA 26 25 NA 0.860 7.200 23.14 37.900 0.400 100.0 0 1 0 0 1 0 0 120 70 18 20 20.0 19.00 7.80
522 0 40 78.0 155.000 32.5 15 74 20 11.20 2 5 14.0 0 0 212.750 1.990 9.140 3.220 NA 48 46 NA 2.090 2.500 25.04 23.400 0.250 92.0 0 1 0 0 1 0 0 120 80 8 10 19.0 21.00 8.00
523 0 29 52.0 154.000 21.9 13 72 18 11.80 2 5 8.0 1 0 157.680 1.990 7.080 3.260 NA 28 25 NA 2.780 18.500 16.26 19.000 0.570 140.0 1 0 1 1 0 0 0 110 80 6 5 17.5 16.50 10.00
524 1 36 60.0 150.000 26.7 15 72 18 10.20 4 7 8.0 0 0 2.140 1.990 5.710 1.000 NA 42 40 NA 2.110 7.700 39.41 26.100 0.250 110.0 1 0 0 0 1 0 0 110 80 6 9 17.0 22.00 7.60
525 1 27 70.0 170.688 24.0 13 72 18 11.50 2 5 4.0 1 2 1.990 1.990 4.300 2.040 NA 44 42 NA 3.650 0.190 9.83 21.800 0.500 107.0 1 0 0 0 0 0 0 120 80 9 10 21.0 23.00 6.50
526 0 27 48.0 158.496 19.1 13 72 18 10.70 2 5 2.0 0 0 1.990 1.990 8.950 4.610 NA 26 24 NA 4.270 0.600 19.46 24.300 0.250 100.0 0 1 0 0 0 0 0 110 70 2 3 16.0 19.00 7.80
527 0 32 60.0 155.000 25.0 16 74 22 10.50 4 7 4.0 1 1 83.950 1.990 3.340 2.610 NA 42 40 NA 1.520 2.500 11.20 12.700 0.250 107.0 0 1 0 0 0 0 0 120 80 5 3 6.8 12.00 6.80
528 0 22 62.0 162.000 23.6 15 74 22 11.10 2 4 3.0 0 0 18.750 1.990 4.360 2.660 NA 44 42 NA 2.080 3.700 28.75 21.400 0.250 115.0 0 0 0 0 1 0 0 120 80 10 9 21.0 18.00 6.20
529 0 41 58.0 146.304 27.1 13 72 20 10.50 2 6 6.0 0 0 1.990 1.990 9.640 3.680 NA 42 40 NA 2.520 0.800 16.21 31.000 0.250 92.0 1 0 0 0 0 1 0 120 80 1 0 19.0 14.00 9.70
530 0 32 52.0 163.000 19.6 13 70 24 10.80 2 6 4.0 0 0 1.990 1.990 1.350 0.370 NA 30 28 NA 5.040 2.300 14.86 19.300 0.250 108.0 0 0 0 0 0 1 0 120 80 3 1 18.0 17.00 7.30
531 0 38 61.0 158.000 24.4 15 72 20 12.30 2 5 10.0 0 0 270.490 1.990 9.290 2.540 NA 38 34 NA 1.310 1.000 19.31 30.500 0.260 100.0 0 0 0 0 0 0 0 120 80 10 11 21.0 12.00 4.80
532 0 34 70.0 164.000 26.0 11 72 22 10.50 2 5 8.0 0 0 1.990 1.990 6.870 2.070 NA 48 44 NA 1.080 0.700 39.32 11.300 0.250 92.0 0 0 0 0 0 0 0 120 70 2 0 21.0 16.00 8.50
533 0 39 56.0 158.496 22.3 12 72 22 10.80 2 4 13.0 0 0 667.070 1.990 10.290 5.100 NA 32 28 NA 1.480 6.300 32.14 23.400 0.250 92.0 0 0 0 0 0 0 0 130 80 9 7 17.0 18.00 7.00
534 1 26 53.5 161.544 20.5 14 70 18 10.60 4 7 3.0 0 0 572.860 5.810 3.710 4.660 NA 28 26 NA 1.400 19.600 29.57 32.100 0.360 100.0 0 0 0 0 1 0 0 110 80 8 10 18.0 18.00 10.30
535 0 24 48.0 158.496 19.1 14 70 18 11.00 2 5 4.0 0 0 615.290 1.990 3.740 9.380 NA 30 28 NA 1.690 18.200 33.87 26.900 0.300 92.0 0 0 0 0 0 0 0 110 80 7 7 20.0 19.00 8.20
536 0 26 80.0 161.544 30.7 18 70 18 10.60 2 5 4.0 0 0 1.990 1.990 7.060 3.500 NA 48 44 NA 17.200 7.600 39.01 32.700 0.250 92.0 0 0 0 0 0 0 0 110 80 7 9 13.0 17.50 9.60
537 0 35 50.0 164.592 18.5 17 72 16 11.00 2 5 8.0 0 1 1.990 1.990 10.060 1.810 NA 28 26 NA 1.110 1.700 5.30 36.600 0.250 92.0 0 0 0 0 0 0 0 110 70 1 0 17.5 10.00 6.70
538 0 30 63.2 158.000 25.3 15 72 18 10.80 2 5 4.0 1 1 80.130 1.990 5.070 2.840 NA 34 32 NA 2.050 5.600 21.09 23.000 0.250 108.0 1 0 0 0 0 0 0 110 70 9 7 19.0 18.00 8.20
539 0 36 54.0 152.000 23.4 13 74 20 10.80 2 6 8.0 0 0 1.990 1.990 11.960 2.780 NA 30 28 NA 2.870 3.700 96.41 22.500 0.250 92.0 0 0 0 0 0 0 0 110 80 1 0 18.0 9.00 7.30
540 0 27 50.0 150.000 22.2 15 74 20 12.00 4 2 2.0 0 0 292.920 1.990 4.400 4.330 NA 28 26 NA 2.500 5.200 38.89 22.400 0.250 115.0 0 0 0 0 1 0 0 110 70 7 6 18.0 16.00 11.50
541 1 23 82.0 165.000 30.1 13 80 20 10.20 4 7 2.0 0 0 1.990 1.990 3.990 4.300 NA 48 46 NA 1.660 20.000 20.74 17.400 0.370 108.0 1 1 1 1 1 1 0 120 70 9 10 19.0 18.00 6.90

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In the second part, I addressed missing values by initially converting Height from centimeters to meters, Hip and Waist from inches to cm and computing BMI, Waist_Hip_Ratio, and FSH_LH. Following a thorough assessment of the dataset, I opted to substitute missing values in Married_yrs, AMH_ngmL, and Fast_food with the median value as it didn’t significantly affect the data’s distribution.

\(~\)

The final results are seen in pcos_cleaned below:

Table 4.pcos_cleaned dataset
PCOS Age_yrs Weight Height BMI Blood_Group Pulse_rate_bpm RR_breaths_min Hb_gdl Cycle_RI Cycle_length_days Married_yrs Pregnant No_of_abortions IbetaHCG_mIUmL IIbetaHCG_mIUmL FSH_mIUmL LH_mIUmL FSH_LH Hip Waist Waist_Hip_Ratio TSH_mIUmL AMH_ngmL PRL_ngmL Vit_D3_ngmL PRG_ngmL RBS_mgdl Weight_gain Hair_growth Skin_darkening Hair_loss Pimples Fast_food Reg_Exercise BP_Systolic_mmHg BP_Diastolic_mmHg Follicle_NoL Follicle_NoR Avg_F_sizeL_mm Avg_F_sizeR_mm Endometrium_mm
0 28 44.6 1.5 19.8 15 78 22 10.5 2 5 7.0 0 0 2.0 1.990 8.0 3.7 2.2 91.4 76.2 0.83 0.7 2.1 45.2 17.1 0.6 92.0 0 0 0 0 0 1 0 110 80 3 3 18.0 18.0 8.5
0 36 65.0 1.6 25.4 15 74 20 11.7 2 5 11.0 1 0 60.8 1.990 6.7 1.1 6.2 96.5 81.3 0.84 3.2 1.5 20.1 61.3 1.0 92.0 0 0 0 0 0 0 0 120 70 3 5 15.0 14.0 3.7
1 33 68.8 1.7 23.8 11 72 18 11.8 2 5 10.0 1 0 494.1 494.080 5.5 0.9 6.3 101.6 91.4 0.90 2.5 6.6 10.5 49.7 0.4 84.0 0 0 0 1 1 1 0 120 80 13 15 18.0 20.0 10.0
0 37 65.0 1.5 28.9 13 72 20 12.0 2 5 4.0 0 0 2.0 1.990 8.1 2.4 3.4 106.7 91.4 0.86 16.4 1.2 36.9 33.4 0.4 76.0 0 0 0 0 0 0 0 120 70 2 2 15.0 14.0 7.5
0 25 52.0 1.6 20.3 11 72 18 10.0 2 5 1.0 1 0 801.5 801.450 4.0 0.9 4.4 94.0 76.2 0.81 3.6 2.3 30.1 43.8 0.4 84.0 0 0 0 1 0 0 0 120 80 3 4 16.0 14.0 7.0
0 36 74.1 1.7 25.6 15 78 28 11.2 2 5 8.0 1 0 238.0 1.990 3.2 1.1 3.0 111.8 96.5 0.86 1.6 6.7 16.2 52.4 0.3 76.0 1 0 0 1 0 0 0 110 70 9 6 16.0 20.0 8.0
0 34 64.0 1.6 25.0 11 72 18 10.9 2 5 2.0 0 0 2.0 1.990 2.9 0.3 9.2 99.1 83.8 0.85 1.5 3.0 26.4 42.7 0.5 93.0 0 0 0 0 0 0 0 120 80 6 6 15.0 16.0 6.8
0 33 58.5 1.6 22.9 13 72 20 11.0 2 5 13.0 1 2 100.5 100.510 4.9 3.1 1.6 111.8 96.5 0.86 12.2 1.5 4.0 38.0 0.3 91.0 1 0 0 0 0 0 0 120 80 7 6 15.0 18.0 7.1
0 32 40.0 1.6 15.6 11 72 18 11.8 2 5 8.0 0 1 2.0 1.990 3.8 3.0 1.2 99.1 88.9 0.90 1.5 1.0 19.0 21.8 0.3 116.0 0 0 0 0 0 0 0 120 80 5 7 17.0 17.0 4.2
0 36 52.0 1.5 23.1 15 80 20 10.0 4 2 4.0 0 0 2.0 1.990 2.8 1.5 1.9 101.6 96.5 0.95 6.7 1.6 11.7 27.7 0.2 125.0 0 0 0 0 0 0 0 110 80 1 1 14.0 17.0 2.5
0 20 71.0 1.6 27.7 15 80 20 10.0 2 5 4.0 1 2 158.5 158.510 4.9 2.0 2.4 99.1 88.9 0.90 1.6 4.5 13.5 18.1 0.4 108.0 0 0 0 0 0 0 0 110 80 7 15 17.0 20.0 6.0
0 26 49.0 1.6 19.1 13 72 20 9.5 2 5 3.0 0 1 2.0 1.990 4.1 1.5 2.8 99.1 83.8 0.85 4.0 1.7 21.1 29.2 0.2 100.0 0 0 0 0 0 0 0 120 80 4 2 18.0 19.0 7.8
1 25 74.0 1.5 32.9 17 72 18 11.7 4 2 7.0 1 0 1214.2 1214.230 2.0 1.5 1.3 114.3 101.6 0.89 6.5 7.9 22.4 31.4 0.3 125.0 1 1 1 1 1 1 1 120 80 15 8 20.0 21.0 8.0
0 38 50.0 1.5 22.2 13 74 20 12.1 2 5 15.0 0 0 2.0 1.990 4.8 0.7 6.8 99.1 83.8 0.85 1.5 2.4 15.6 21.2 0.4 91.0 0 0 0 0 0 0 0 110 70 3 3 18.0 17.0 5.6
0 34 57.3 1.6 22.4 13 74 22 11.7 2 5 9.0 0 0 2.0 1.990 7.4 3.7 2.0 96.5 76.2 0.79 1.5 0.9 19.6 24.9 0.3 116.0 0 0 0 1 1 1 0 120 80 4 1 19.0 21.0 5.5
0 38 80.5 1.5 35.8 13 78 22 11.4 2 5 20.0 0 0 2.0 1.990 9.5 2.5 3.8 111.8 104.1 0.93 1.2 0.7 92.7 9.7 0.3 116.0 1 0 0 0 0 0 0 120 80 1 3 14.0 20.0 3.9
0 29 43.0 1.5 19.1 13 80 20 11.1 2 5 2.0 1 0 8104.2 91.550 2.0 0.7 3.1 91.4 73.7 0.81 2.0 3.8 20.2 19.1 0.5 91.0 0 0 0 0 0 0 0 110 70 6 5 20.0 20.0 5.6
0 36 69.2 1.6 27.0 13 72 18 10.8 2 5 7.0 0 0 2.0 1.990 4.9 3.0 1.6 99.1 81.3 0.82 5.0 1.9 12.5 26.0 0.2 100.0 0 0 0 0 0 0 0 110 80 1 2 20.0 18.0 4.0
0 31 52.4 1.6 20.5 17 72 18 12.7 2 5 7.0 0 0 2.0 1.990 6.0 1.0 5.8 94.0 83.8 0.89 3.2 1.0 12.1 23.3 0.2 127.0 0 0 0 0 0 0 0 120 80 0 2 0.0 17.0 5.6
1 30 85.0 1.7 29.4 16 72 18 12.5 4 7 7.0 0 0 23.6 1.990 1.9 0.8 2.3 111.8 106.7 0.95 2.9 2.1 19.1 28.0 0.3 100.0 0 1 1 1 1 1 0 120 80 16 8 18.0 17.0 11.0
0 25 64.0 1.6 25.0 11 70 18 11.2 2 6 6.0 0 0 2.0 1.990 2.8 1.3 2.2 99.1 86.4 0.87 1.9 2.9 33.6 17.1 0.4 91.0 0 0 0 0 0 0 0 110 80 1 2 18.0 19.0 5.7
0 38 50.0 1.6 19.5 15 72 18 11.0 4 9 12.0 1 0 750.0 749.980 3.2 2.2 1.5 91.4 73.7 0.81 5.7 2.1 40.7 18.9 0.3 84.0 0 0 0 0 0 0 0 120 80 4 2 17.0 17.0 7.6
0 34 64.2 1.6 25.1 15 74 20 12.1 2 5 10.0 1 0 218.7 218.650 4.1 2.3 1.8 91.4 81.3 0.89 1.2 4.1 13.4 28.6 0.3 116.0 0 0 0 0 0 0 0 110 70 5 7 16.0 17.0 7.6
0 28 65.0 1.5 28.9 13 74 22 10.5 2 5 5.0 1 0 13.0 13.000 6.4 1.7 3.8 94.0 83.8 0.89 0.4 2.5 17.9 30.5 0.7 92.0 0 0 0 0 1 0 0 110 80 6 8 14.0 18.0 8.5
1 34 63.0 1.6 24.6 11 72 20 11.2 2 5 12.0 1 0 610.6 610.630 5.3 0.9 6.0 96.5 81.3 0.84 0.7 1.9 11.5 21.5 1.0 100.0 0 1 0 0 1 1 0 120 70 4 6 18.0 17.0 7.3
0 41 42.0 1.5 18.7 12 80 20 11.5 2 5 15.0 1 0 4490.2 4490.180 8.8 4.4 2.0 99.1 81.3 0.82 0.8 0.3 17.4 45.6 1.0 92.0 0 0 0 0 0 0 0 110 70 1 2 15.0 17.0 8.8
1 30 76.0 1.6 29.7 15 75 18 11.2 4 3 5.0 0 0 2.0 1.990 6.2 2.8 2.2 114.3 96.5 0.84 4.3 3.8 18.0 25.1 0.3 100.0 1 1 1 1 1 1 1 120 80 21 20 11.0 12.0 6.8
0 20 68.0 1.5 30.2 17 72 20 10.0 4 3 4.0 1 0 689.6 11.240 1.8 0.4 4.4 101.6 83.8 0.82 8.4 2.5 11.2 46.3 1.2 92.0 0 0 0 0 0 0 0 110 80 3 2 10.0 11.0 10.0
0 25 62.0 1.6 24.2 15 78 22 11.6 2 5 8.0 0 0 15.0 15.000 4.6 1.1 4.3 106.7 86.4 0.81 3.8 3.6 15.3 36.6 0.2 106.0 0 0 0 0 0 0 0 120 80 7 4 12.0 17.0 6.2
1 28 56.0 1.5 24.9 13 78 22 11.0 4 3 7.0 0 0 768.0 768.030 2.4 1.2 2.0 101.6 83.8 0.82 3.4 1.6 45.5 23.1 0.6 100.0 1 0 1 0 1 1 0 110 80 11 10 13.0 14.0 5.0
0 32 50.0 1.6 19.5 17 78 22 10.8 2 4 3.5 1 0 612.3 12.000 6.3 4.1 1.6 86.4 71.1 0.82 1.4 1.7 54.1 56.2 0.3 80.0 0 0 0 0 0 0 0 110 80 10 8 14.0 14.0 7.0
1 34 57.0 1.5 25.3 11 72 22 12.0 2 4 15.0 0 0 10.0 10.000 5.0 6.5 0.8 101.6 94.0 0.93 4.2 1.9 12.7 32.5 0.3 85.0 1 1 1 1 1 1 0 110 80 5 10 14.0 13.0 6.0
0 31 58.0 1.6 22.7 15 72 18 10.5 2 5 2.0 0 0 2.0 1.990 2.8 0.4 6.7 99.1 81.3 0.82 1.9 2.3 23.7 32.2 0.5 138.0 0 0 0 0 0 0 0 120 80 8 9 12.0 13.0 7.5
0 38 54.0 1.7 18.7 15 72 18 10.2 2 5 15.0 0 3 20.0 20.000 6.1 2.0 3.1 106.7 86.4 0.81 2.4 1.6 40.4 36.9 0.6 92.0 0 0 0 0 0 0 0 120 80 2 5 14.0 12.0 8.5
0 28 50.0 1.6 19.5 15 76 20 10.0 2 5 6.0 1 0 1277.5 30.660 2.2 0.6 3.8 81.3 68.6 0.84 1.7 2.4 55.2 36.9 0.6 85.0 0 0 0 0 0 0 0 120 70 11 7 14.0 19.0 8.0
0 32 71.0 1.7 24.6 11 72 22 12.0 2 5 10.0 1 0 1455.0 1455.000 2.6 0.5 5.1 106.7 91.4 0.86 2.0 3.6 19.8 25.2 0.4 80.0 0 0 0 0 0 0 0 110 80 1 1 11.0 12.0 7.0
0 37 73.0 1.6 28.5 15 74 18 10.5 4 7 12.0 0 0 18.0 1.990 3.2 0.5 6.7 114.3 96.5 0.84 3.6 2.8 28.9 36.6 0.3 92.0 0 0 0 0 0 0 0 110 80 6 2 11.0 13.0 10.0
0 26 72.0 1.6 28.1 11 70 18 11.0 2 5 1.0 0 0 12.0 1.990 6.0 2.3 2.6 106.7 86.4 0.81 65.0 0.9 30.6 20.2 0.9 90.0 0 0 0 0 0 0 0 120 80 1 2 6.5 8.5 10.0
0 36 53.0 1.6 20.7 13 78 22 11.0 2 5 17.0 0 0 10.0 1.990 4.0 2.0 2.0 99.1 81.3 0.82 2.4 2.4 19.6 45.5 0.7 100.0 0 0 0 0 0 0 0 110 80 7 5 8.0 7.0 15.0
0 20 74.0 1.7 25.6 13 74 16 11.1 4 0 1.0 0 0 2.0 1.990 3.5 9.2 0.4 106.7 91.4 0.86 2.0 0.3 12.5 38.6 0.3 80.0 0 0 0 0 0 0 0 110 70 6 12 13.0 12.0 0.0
0 32 40.0 1.6 15.6 15 78 22 10.5 2 5 6.0 0 0 10.0 1.990 7.9 4.9 1.6 86.4 76.2 0.88 5.0 2.4 31.3 56.2 0.2 107.0 0 0 0 0 0 0 0 110 80 5 8 14.0 14.0 7.3
0 29 78.0 1.7 27.0 13 72 16 12.8 2 5 2.0 1 0 497.4 497.410 5.6 2.1 2.6 106.7 91.4 0.86 0.9 3.9 20.5 14.4 85.0 92.0 0 0 0 0 0 0 0 110 80 8 10 11.0 12.0 6.5
0 28 33.0 1.6 12.9 13 78 18 11.5 4 9 3.5 0 0 10.0 1.990 3.3 0.2 13.4 81.3 66.0 0.81 3.4 3.5 6.6 42.3 0.2 84.0 0 0 0 0 0 0 0 100 70 0 1 0.0 12.0 6.5
0 24 68.0 1.7 23.5 15 72 16 11.0 2 5 3.5 1 0 161.1 167.000 2.8 2.0 1.4 99.1 83.8 0.85 1.5 4.3 0.4 25.4 0.2 100.0 0 0 0 0 0 0 0 110 70 6 8 8.0 8.0 7.1
1 29 59.0 1.6 23.0 17 78 22 12.0 4 7 7.0 1 0 1305.3 1.990 3.3 0.8 4.1 99.1 83.8 0.85 3.8 3.5 31.4 44.5 0.8 100.0 1 1 1 1 1 1 1 120 80 13 14 16.0 15.0 7.0
0 25 56.0 1.6 21.9 15 70 18 10.6 2 5 6.0 1 0 43.5 9.830 2.8 0.6 4.9 101.6 83.8 0.82 3.9 2.4 25.6 45.5 0.3 92.0 0 0 0 0 0 0 0 120 80 5 8 14.0 13.0 9.4
1 28 75.0 1.7 26.0 15 72 18 12.0 2 5 2.5 1 0 141.1 141.060 5.8 0.3 21.5 114.3 104.1 0.91 0.8 3.7 14.4 52.3 0.4 100.0 1 1 0 1 1 1 0 110 70 4 6 10.0 12.0 9.0
0 26 40.0 1.6 15.6 13 74 18 10.2 2 5 3.0 1 0 1500.0 528.500 7.3 2.8 2.6 99.1 83.8 0.85 4.4 4.3 24.6 54.6 0.2 80.0 0 0 0 0 0 0 0 110 70 5 7 15.0 18.0 16.0
0 34 51.0 1.6 19.9 11 78 24 10.8 2 5 11.0 0 0 10.0 1.990 2.5 0.5 4.8 99.1 86.4 0.87 2.2 1.0 8.0 35.7 0.2 75.0 0 0 0 0 0 1 0 110 80 6 8 10.0 15.0 9.0
1 27 51.0 1.5 22.7 15 72 18 12.8 4 3 4.0 0 0 10.0 1.990 2.3 2.4 0.9 96.5 86.4 0.90 2.8 4.5 15.2 45.2 0.5 100.0 1 1 1 1 1 1 1 110 70 5 8 14.0 13.0 10.4
1 23 68.0 1.7 23.5 15 72 16 10.8 4 10 4.0 0 0 77.6 1.990 4.4 5.5 0.8 106.7 86.4 0.81 3.3 3.2 27.6 23.1 0.4 70.0 1 1 1 1 1 0 0 110 70 22 18 12.0 13.0 11.0
0 23 54.0 1.6 21.1 16 72 18 10.8 2 5 2.0 1 0 177.6 177.570 5.3 1.8 3.0 96.5 81.3 0.84 5.2 2.1 24.5 14.0 0.2 70.0 0 0 0 0 0 0 0 100 70 7 10 15.0 13.0 10.0
1 31 50.0 1.6 19.5 15 72 20 10.8 2 4 1.5 1 0 160.8 10.000 7.1 4.1 1.7 99.1 81.3 0.82 2.2 4.5 21.4 64.2 0.6 107.0 1 0 1 0 1 1 1 120 80 6 11 12.0 11.0 6.0
0 32 62.0 1.5 27.6 15 70 18 10.5 2 5 13.0 1 0 180.3 12.000 2.5 0.7 3.5 114.3 99.1 0.87 0.8 6.6 18.5 45.4 0.2 92.0 0 0 0 0 0 0 0 120 80 4 6 13.0 14.0 6.8
0 32 58.0 1.5 25.8 13 70 18 10.0 2 5 16.0 0 0 2.0 1.990 9.4 6.1 1.6 101.6 91.4 0.90 2.8 1.2 33.8 56.2 0.4 100.0 0 0 0 0 0 0 0 120 80 5 9 10.0 11.0 6.3
0 37 68.0 1.7 23.5 15 72 18 10.0 2 5 7.0 1 0 274.7 15.000 3.5 1.0 3.6 111.8 94.0 0.84 5.2 2.3 34.4 25.6 0.2 92.0 0 0 0 0 0 0 0 110 80 6 7 9.5 11.0 8.9
0 38 56.0 1.6 21.9 11 72 20 10.8 2 5 15.0 1 0 2.0 1.990 8.5 2.4 3.5 101.6 86.4 0.85 4.4 3.2 36.5 45.3 0.4 130.0 0 0 0 0 0 0 0 120 80 6 5 11.0 13.0 8.6
0 36 58.0 1.5 25.8 14 72 20 10.0 2 5 13.0 1 1 249.9 12.000 3.9 0.6 6.4 106.7 88.9 0.83 2.0 2.3 13.5 31.2 0.5 82.0 0 0 0 0 0 0 0 110 80 3 2 12.0 13.0 9.3
0 26 75.0 1.6 29.3 13 72 18 10.5 2 5 8.0 0 0 2.0 1.990 6.9 3.3 2.1 114.3 101.6 0.89 2.3 2.3 9.3 25.6 0.3 92.0 0 0 0 0 0 0 0 110 80 3 3 16.0 17.0 14.0
0 26 67.0 1.4 34.2 15 74 20 11.0 4 9 12.0 0 0 2.0 1.990 5.9 2.7 2.2 111.8 99.1 0.89 1.7 2.4 15.2 45.4 0.7 92.0 0 0 0 0 0 0 0 120 70 5 7 14.0 15.0 11.0
0 29 63.0 1.6 24.6 13 78 22 11.2 2 5 4.0 1 0 626.9 65.030 6.1 3.9 1.6 106.7 91.4 0.86 6.7 4.2 47.7 36.2 0.6 92.0 0 0 0 0 0 0 0 110 80 6 7 13.0 14.0 7.2
1 32 76.0 1.6 29.7 15 78 22 11.2 2 5 12.0 0 1 8.0 1.990 1.5 0.1 9.7 114.3 99.1 0.87 0.4 3.2 13.2 25.4 0.3 107.0 1 1 1 1 1 1 1 120 80 9 12 14.0 13.0 7.8
0 24 60.0 1.7 20.8 15 70 20 10.0 2 5 3.5 0 0 2.0 1.990 5.8 7.5 0.8 104.1 91.4 0.88 3.6 2.1 14.7 36.2 0.8 100.0 0 0 0 0 0 0 0 120 80 3 5 12.0 13.5 9.6
0 29 55.0 1.5 24.4 15 80 20 10.2 4 2 6.0 1 0 757.2 20.000 6.8 9.3 0.7 99.1 86.4 0.87 1.2 2.3 20.8 50.2 0.6 92.0 0 0 0 0 0 0 0 120 70 4 7 11.0 11.0 8.6
0 27 56.0 1.5 24.9 11 72 18 10.5 4 8 6.0 0 0 2.0 1.990 6.3 7.5 0.8 101.6 88.9 0.88 3.7 4.6 37.9 49.3 3.7 92.0 0 0 0 0 0 0 0 110 80 11 10 14.5 14.0 12.5
0 32 71.0 1.7 24.6 12 70 18 11.0 2 5 8.0 0 1 10.0 1.990 4.8 1.5 3.2 116.8 99.1 0.85 2.5 2.3 22.4 51.9 0.4 100.0 0 0 0 0 0 0 0 100 70 2 4 12.0 14.0 9.4
0 41 56.0 1.6 21.9 13 78 20 11.5 2 5 4.0 0 3 31.3 1.990 9.7 2.4 4.1 96.5 86.4 0.90 3.3 5.8 54.3 36.5 0.3 115.0 0 0 0 0 0 0 0 120 80 1 2 10.0 12.0 9.7
0 35 62.0 1.5 27.6 16 78 22 8.5 2 5 12.0 1 1 218.6 1.990 5.4 8.4 0.6 99.1 83.8 0.85 9.0 5.2 6.3 25.4 0.6 120.0 0 0 0 0 0 0 0 110 80 14 12 8.0 5.0 5.5
1 35 55.0 1.5 24.4 17 80 20 10.3 2 5 18.0 1 2 218.6 1.990 1.6 0.8 2.0 96.5 86.4 0.90 4.2 2.1 23.4 56.2 0.4 100.0 1 1 1 1 1 0 0 120 70 7 9 5.0 8.5 6.2
1 34 65.0 1.7 22.5 15 80 20 11.0 2 5 9.0 1 0 173.7 173.660 2.2 0.8 2.7 104.1 91.4 0.88 2.9 4.6 28.9 15.4 0.2 80.0 1 0 1 0 1 0 1 120 70 11 8 9.5 7.0 8.9
0 33 54.0 1.5 24.0 13 72 18 10.2 2 5 9.0 0 0 4.4 1.990 4.8 0.7 6.7 101.6 83.8 0.82 2.0 1.0 15.6 42.4 0.5 92.0 0 0 0 0 0 0 0 100 70 5 2 11.5 4.7 6.5
0 29 61.0 1.5 27.1 13 74 20 10.0 2 5 9.0 0 0 15.0 1.990 6.5 1.0 6.6 106.7 88.9 0.83 1.7 2.6 41.6 38.4 1.2 92.0 0 0 0 0 0 0 0 120 70 5 6 12.0 6.0 6.0
0 36 65.0 1.5 28.9 15 70 18 10.0 2 5 3.0 1 0 161.5 161.490 5.0 5.3 0.9 109.2 96.5 0.88 3.2 5.8 32.1 56.5 0.8 92.0 0 0 0 0 0 0 0 110 80 3 1 7.5 4.5 5.4
0 26 70.0 1.6 27.3 15 72 18 10.5 2 5 3.0 0 2 2.0 1.990 7.3 6.7 1.1 114.3 101.6 0.89 3.0 0.3 22.9 14.4 0.3 92.0 1 0 0 0 0 0 0 110 80 3 4 10.5 6.0 9.8
0 35 59.0 1.5 26.2 13 72 20 11.1 4 12 10.0 1 2 603.8 15.000 5.5 4.3 1.3 106.7 91.4 0.86 4.0 5.2 32.0 37.6 0.2 93.0 0 0 0 0 0 0 0 120 80 2 3 9.5 6.2 8.7
0 36 72.0 1.5 32.0 13 72 18 10.2 2 4 10.0 1 0 486.4 3.980 6.7 2.8 2.4 111.8 94.0 0.84 2.2 3.7 19.8 10.7 0.3 100.0 0 0 0 0 0 0 0 110 80 0 2 0.0 5.8 8.5
1 32 43.0 1.5 19.1 13 80 20 10.5 4 11 6.0 1 0 756.6 756.610 5.0 2.1 2.4 101.6 86.4 0.85 4.4 2.1 25.5 38.2 0.3 92.0 0 1 1 1 0 1 0 120 70 4 4 10.5 10.5 12.8
0 34 52.0 1.5 23.1 11 72 18 12.0 2 5 0.0 0 0 2.0 1.990 5.3 2.1 2.5 104.1 91.4 0.88 4.6 2.5 16.1 36.0 0.1 80.0 0 0 0 0 0 0 0 110 70 7 4 15.0 12.0 18.0
0 37 48.0 1.5 21.3 11 75 20 10.2 2 5 2.0 0 1 2.0 1.990 15.4 4.4 3.5 96.5 86.4 0.90 1.2 4.9 17.0 29.5 0.5 98.0 0 0 0 0 0 0 0 110 70 0 2 0.0 9.0 8.6
0 38 108.0 1.7 37.4 14 80 20 11.0 2 5 12.0 0 3 10.0 1.990 5.0 2.6 1.9 121.9 104.1 0.85 1.9 1.0 27.3 36.9 0.5 100.0 0 0 0 0 0 0 0 110 70 1 2 8.0 9.0 10.0
0 36 57.6 1.5 25.6 11 72 18 11.8 4 2 10.0 0 0 2.0 1.990 6.2 3.2 1.9 99.1 86.4 0.87 2.0 6.6 4.2 27.8 0.3 100.0 0 0 0 0 0 0 0 120 70 8 6 13.0 15.0 8.5
0 34 60.0 1.6 23.4 13 72 16 12.5 2 5 7.0 1 0 255.0 255.020 3.4 0.4 8.3 101.6 91.4 0.90 1.5 3.9 21.1 29.8 0.3 133.0 0 0 0 0 0 0 0 120 80 8 6 16.0 15.0 8.3
0 39 52.0 1.6 20.3 11 78 20 10.8 2 5 9.0 0 0 2.0 1.990 4.8 7.6 0.6 99.1 88.9 0.90 3.6 5.4 8.2 21.3 0.4 91.0 0 0 0 0 0 0 0 110 80 2 2 15.0 17.0 0.0
0 35 43.7 1.5 19.4 11 72 20 10.4 2 5 8.0 0 0 2.0 1.990 6.4 1.9 3.3 101.6 86.4 0.85 0.6 1.6 20.3 38.2 0.3 108.0 0 0 0 0 0 0 0 120 80 2 6 14.0 18.0 6.0
0 34 60.0 1.6 23.4 14 74 20 12.5 2 5 7.0 0 0 2.0 1.990 6.5 2.2 3.0 99.1 81.3 0.82 4.2 2.1 22.1 37.1 0.3 133.0 0 0 0 0 0 0 0 120 70 2 2 13.0 15.0 10.0
0 27 61.6 1.5 27.4 15 70 18 11.2 2 5 7.0 1 0 273.7 273.700 3.6 1.1 3.4 104.1 91.4 0.88 1.5 1.8 20.4 28.5 0.3 91.0 0 0 0 0 0 0 0 120 80 4 4 16.0 13.0 12.0
0 31 64.0 1.6 25.0 15 74 18 12.1 4 9 5.0 0 0 2.0 1.990 2.3 0.4 6.0 106.7 91.4 0.86 2.7 3.8 22.5 29.8 0.3 84.0 0 0 0 0 0 0 0 120 80 8 10 15.0 13.0 10.0
0 40 56.0 1.6 21.9 11 80 20 10.3 2 5 8.0 1 0 162.1 1.990 1.0 0.0 50.0 96.5 86.4 0.90 1.5 2.3 28.1 55.9 0.8 125.0 0 0 0 0 0 0 0 120 70 11 9 17.0 18.0 4.6
1 22 69.5 1.7 24.0 13 74 20 12.5 4 2 3.0 0 0 2.0 1.990 5.7 1.4 4.0 111.8 99.1 0.89 1.3 3.6 44.9 27.7 0.2 100.0 0 0 0 0 0 0 0 110 80 10 13 15.0 16.0 10.0
0 36 65.0 1.5 28.9 15 72 18 10.2 2 5 11.0 0 0 2.0 1.990 4.2 1.2 3.5 104.1 91.4 0.88 2.8 9.0 19.3 11.3 0.6 134.0 0 0 0 0 0 0 0 120 70 11 9 15.0 16.0 8.0
0 44 74.4 1.6 29.1 17 78 22 11.1 2 5 22.0 0 2 2.0 1.990 4.2 3.6 1.1 121.9 106.7 0.88 4.0 1.7 7.7 34.7 0.3 109.0 0 0 0 0 0 0 0 110 80 4 2 15.0 7.0 6.0
0 35 45.0 1.5 20.0 15 74 20 11.8 2 5 6.0 0 0 2.0 1.990 3.2 1.6 2.1 106.7 91.4 0.86 1.8 3.2 15.8 34.2 0.3 116.0 0 0 0 0 0 0 0 120 80 7 5 20.0 18.0 5.7
0 28 83.5 1.6 32.6 15 78 22 12.5 4 11 8.0 0 0 2.0 1.990 4.4 2.7 1.6 96.5 83.8 0.87 2.6 2.8 13.8 29.4 0.3 116.0 0 0 0 0 0 0 0 110 80 2 2 14.0 15.0 8.3
0 32 52.0 1.6 20.3 11 72 20 10.8 2 5 3.0 0 0 2.0 1.990 5.1 9.0 0.6 96.5 81.3 0.84 4.3 0.9 20.0 29.8 0.4 97.0 0 0 0 0 0 0 0 120 80 2 1 14.0 15.0 9.0
0 27 62.5 1.5 27.8 14 70 20 12.4 2 5 5.0 0 0 2.0 1.990 5.3 2.8 1.9 104.1 86.4 0.83 6.8 2.3 27.8 22.5 0.4 116.0 0 0 0 0 0 0 0 120 80 3 5 20.0 19.0 6.8
1 22 50.0 1.6 19.5 15 80 20 12.1 2 5 1.0 1 0 1247.6 15.000 2.0 1.0 2.0 96.5 81.3 0.84 2.8 2.2 27.9 18.7 0.3 107.0 1 1 1 1 1 1 0 120 70 6 10 19.0 19.0 6.0
1 28 67.5 1.5 30.0 16 78 24 11.7 2 5 2.0 0 3 15.0 1.990 4.7 2.4 1.9 104.1 91.4 0.88 4.6 8.5 28.9 18.7 0.4 104.0 0 0 0 0 0 0 0 110 70 18 13 17.0 18.0 5.9
1 31 91.4 1.5 40.6 15 78 24 10.7 4 12 10.0 1 0 320.6 14.460 3.7 2.1 1.8 99.1 81.3 0.82 5.0 4.6 40.0 28.3 0.4 108.0 1 1 1 1 1 1 0 110 70 13 10 19.0 16.0 5.0
0 32 61.0 1.6 23.8 11 72 20 11.0 2 5 13.0 1 0 145.9 145.890 5.3 4.0 1.3 101.6 99.1 0.98 4.4 1.0 31.0 19.2 0.4 125.0 0 0 0 0 0 0 0 120 70 3 2 20.0 18.0 5.0
0 32 61.7 1.6 24.1 15 78 22 12.2 2 5 8.0 0 5 5.0 1.990 5.6 0.8 7.1 104.1 86.4 0.83 4.1 4.3 13.7 37.2 0.4 60.0 0 0 0 0 0 0 0 110 80 4 2 18.0 18.0 7.5
0 30 62.2 1.6 24.3 13 72 18 10.8 2 5 5.0 1 1 100.1 100.090 1.5 0.3 4.9 106.7 94.0 0.88 1.4 3.9 20.1 16.9 1.0 116.0 0 0 0 0 0 0 0 120 80 7 10 19.0 20.0 6.8
0 34 74.1 1.6 28.9 11 78 22 12.4 4 11 13.0 0 0 2.0 1.990 1.5 0.3 4.9 104.1 86.4 0.83 1.6 1.4 13.2 44.5 0.3 131.0 0 0 0 0 0 0 0 110 80 5 2 17.5 18.0 6.4
1 38 64.3 1.5 28.6 11 78 20 12.9 4 2 16.0 1 1 110.2 110.170 10.5 2.5 4.2 106.7 91.4 0.86 1.3 10.1 14.5 70.5 0.1 83.2 1 1 1 1 1 1 0 110 70 12 12 17.0 9.0 11.3
0 34 57.0 1.6 22.3 15 72 20 11.8 2 5 12.0 1 1 174.4 174.370 9.4 3.7 2.5 96.5 81.3 0.84 4.0 1.0 17.6 24.3 0.4 92.0 0 0 0 0 0 0 0 120 80 1 0 14.0 0.0 7.0
0 29 60.0 1.6 23.4 13 72 18 11.2 4 9 6.0 1 0 5202.6 75.510 7.1 7.4 0.9 99.1 81.3 0.82 5.0 4.1 10.1 23.6 0.4 91.0 0 0 0 0 0 0 0 120 80 11 12 18.0 20.0 7.0
1 25 68.6 1.8 21.2 11 74 20 10.0 2 5 4.0 1 0 600.2 1.990 5.0 4.8 1.0 111.8 96.5 0.86 2.0 3.2 30.7 20.7 0.4 115.0 1 0 1 0 1 0 1 110 70 12 8 18.0 17.5 9.0
0 28 60.0 1.6 23.4 15 72 20 10.8 4 7 3.0 0 0 2.0 1.990 2.9 0.2 13.2 96.5 81.3 0.84 1.8 3.9 6.5 30.5 0.2 92.0 1 1 1 1 1 0 0 120 80 6 4 19.0 16.0 4.4
1 27 54.0 1.6 21.1 13 72 18 11.8 4 8 2.0 0 0 121.0 1.990 2.6 0.3 7.9 111.8 94.0 0.84 1.6 10.0 26.3 34.1 0.2 100.0 1 1 0 1 1 1 0 110 80 11 10 16.0 14.0 7.0
1 24 53.0 1.5 23.6 15 72 22 13.2 4 9 3.0 1 0 397.1 3893.060 4.6 6.0 0.8 114.3 99.1 0.87 1.5 16.9 9.8 15.3 1.0 120.0 1 1 1 0 0 1 1 110 70 11 10 15.0 16.0 9.0
0 34 80.0 1.6 31.2 15 74 20 10.0 4 11 19.0 1 0 926.2 600.230 4.9 2.0 2.5 106.7 94.0 0.88 1.0 17.0 19.4 39.4 0.3 108.0 0 1 1 0 0 1 1 120 70 5 4 17.0 21.0 9.3
0 21 59.0 1.5 26.2 15 72 18 11.2 4 6 2.0 1 0 26290.3 3350.190 4.1 5.1 0.8 104.1 86.4 0.83 1.3 21.9 14.0 33.4 1.0 125.0 0 0 1 1 0 0 1 100 70 1 1 11.0 10.0 8.5
0 26 75.0 1.7 26.0 15 72 18 11.2 4 2 3.0 1 0 32461.0 97.630 6.2 2.2 2.9 101.6 86.4 0.85 2.4 1.6 21.5 25.3 0.3 100.0 0 1 1 1 0 1 1 120 80 5 4 17.0 19.0 10.0
0 39 71.2 1.7 24.6 15 72 20 10.1 2 5 12.0 0 0 14.5 14.400 4.6 3.4 1.4 104.1 91.4 0.88 4.3 3.3 13.9 29.0 0.7 100.0 1 0 0 1 0 0 0 120 80 5 3 19.0 20.0 7.0
1 32 63.0 1.6 24.6 17 72 18 11.0 4 9 9.0 0 0 332.5 2.000 5.0 7.5 0.7 116.8 101.6 0.87 1.5 21.0 46.0 32.9 0.7 92.0 1 0 1 1 1 1 1 110 80 5 6 18.0 21.0 9.0
1 29 74.0 1.6 28.9 15 72 18 11.3 2 5 6.0 0 0 70.2 2.000 4.5 1.9 2.4 114.3 96.5 0.84 1.7 12.7 11.0 22.7 0.7 100.0 1 1 1 1 0 0 1 110 80 14 5 15.0 14.0 8.7
0 29 67.0 1.6 26.2 11 72 18 11.2 2 5 2.0 0 0 177.6 177.580 5.4 0.4 12.8 96.5 81.3 0.84 0.7 1.8 9.1 15.3 0.6 108.0 1 0 1 0 0 0 1 110 70 5 3 15.5 15.0 8.3
0 35 63.0 1.6 24.6 15 80 22 10.7 2 5 14.0 0 0 3.1 12.170 9.8 0.3 28.9 91.4 76.2 0.83 2.3 3.6 30.0 24.3 0.9 100.0 0 1 0 0 1 1 1 110 70 4 4 15.0 14.0 9.8
1 28 68.0 1.6 26.6 13 74 20 11.2 4 9 7.0 0 0 59.4 2.000 5.7 4.3 1.3 114.3 94.0 0.82 2.4 15.0 21.9 18.3 0.3 100.0 1 0 1 1 1 1 1 110 80 21 20 15.0 17.0 11.0
0 33 83.0 1.6 32.4 13 78 22 11.0 2 5 10.0 0 0 100.7 25.300 4.7 1.5 3.2 96.5 78.7 0.82 0.6 5.0 14.7 32.4 0.3 100.0 0 0 1 1 0 1 0 110 80 7 9 18.0 20.0 8.0
0 29 55.0 1.5 24.4 11 72 18 11.2 2 5 3.0 0 0 566.2 100.200 3.6 4.2 0.8 99.1 81.3 0.82 1.7 3.3 12.6 17.5 0.5 100.0 1 0 0 0 0 0 0 110 80 2 4 18.0 20.0 11.7
0 30 63.0 1.6 24.6 15 76 20 10.5 4 9 5.0 0 0 2.0 479.660 7.8 14.7 0.5 101.6 83.8 0.82 2.9 3.3 24.1 56.3 0.4 100.0 0 0 0 0 0 1 1 110 70 3 2 16.0 14.0 8.6
0 33 48.0 1.5 21.3 16 70 18 10.0 2 5 12.0 0 0 357.1 1.900 7.6 0.5 15.8 96.5 76.2 0.79 1.7 3.9 27.4 33.4 6.4 100.0 0 1 0 0 0 0 0 110 80 1 1 15.0 18.0 6.2
1 22 79.0 1.6 30.9 11 72 18 11.3 2 5 4.0 0 0 138.7 586.060 4.1 4.1 1.0 114.3 94.0 0.82 2.9 17.9 21.9 22.9 0.4 92.0 1 0 1 0 1 1 1 110 80 6 10 16.0 15.0 13.4
1 23 40.0 1.5 17.8 15 74 18 10.8 2 5 2.0 0 0 2.0 NA 5.0 5.2 1.0 116.8 96.5 0.83 1.7 19.8 15.2 23.5 0.9 92.0 1 1 1 1 0 1 1 100 70 10 13 18.0 17.0 12.5
1 26 78.0 1.6 30.5 13 78 20 11.0 2 5 4.0 0 0 2.0 1.990 4.9 5.8 0.8 114.3 99.1 0.87 2.3 9.2 31.5 56.2 0.4 100.0 1 0 1 1 1 1 1 120 80 8 10 16.0 18.0 8.5
0 38 54.0 1.6 21.1 13 72 22 10.8 2 5 4.0 0 1 363.1 2.800 8.4 0.8 10.7 104.1 83.8 0.80 2.4 2.4 33.5 13.5 0.5 92.0 1 0 0 0 0 0 0 120 80 7 6 18.0 17.0 12.0
0 40 56.0 1.5 24.9 15 72 18 10.2 2 5 14.0 0 0 84.8 1.990 5.0 1.9 2.6 106.7 83.8 0.79 2.1 4.5 26.3 52.2 0.9 98.0 0 0 0 1 0 0 1 110 80 2 3 18.0 18.0 8.5
0 42 48.0 1.5 21.3 11 72 18 11.2 2 5 8.0 0 0 2.0 15.360 6.8 3.8 1.8 101.6 81.3 0.80 2.0 5.1 18.0 9.2 0.3 100.0 0 0 0 0 0 0 0 110 70 1 0 17.0 15.0 9.0
0 41 54.0 1.5 24.0 15 70 18 11.5 2 5 10.0 0 0 98.9 54.080 7.4 1.5 4.9 96.5 76.2 0.79 15.7 2.4 20.2 25.6 0.3 92.0 0 0 0 0 0 0 1 110 80 0 1 15.0 9.0 14.0
0 40 40.0 1.4 20.4 11 72 18 10.2 2 5 4.0 0 4 2.0 1.990 5.0 0.7 7.2 96.5 78.7 0.82 1.9 0.3 45.6 36.1 0.3 92.0 0 1 0 0 0 0 0 110 80 2 1 19.0 12.0 9.7
1 24 89.0 1.7 30.8 17 72 18 11.2 4 2 4.0 0 0 152.1 152.130 5.9 9.9 0.6 111.8 96.5 0.86 1.7 11.5 42.4 13.0 0.6 91.0 1 0 1 0 1 1 1 120 80 4 8 16.0 18.0 8.0
0 25 53.4 1.6 20.9 11 72 18 10.5 2 5 4.0 0 0 2.0 1.990 6.5 10.8 0.6 91.4 76.2 0.83 1.3 19.3 17.7 36.8 0.4 100.0 0 0 0 0 1 1 0 110 70 3 7 15.0 14.0 11.5
1 22 60.0 1.5 26.7 15 70 18 12.7 2 5 4.0 0 0 2.0 1.990 6.4 5.8 1.1 114.3 86.4 0.76 3.0 8.8 26.7 41.7 0.3 85.0 1 1 1 0 0 1 0 110 80 3 3 16.0 17.0 9.7
1 28 70.0 1.6 27.3 15 70 18 11.0 2 5 5.0 0 1 342.2 1.990 3.4 3.6 0.9 106.7 91.4 0.86 1.1 19.0 21.7 18.8 0.6 120.0 1 1 1 0 0 1 1 110 80 12 9 19.0 18.0 12.0
0 35 68.0 1.5 30.2 15 72 18 12.0 2 5 10.0 0 0 3.0 3.050 4.8 1.1 4.2 96.5 76.2 0.79 2.3 4.3 14.8 19.2 0.4 85.0 0 0 0 0 1 0 0 120 80 3 4 18.0 17.0 9.0
0 30 71.0 1.6 27.7 15 72 18 12.0 2 5 10.0 0 2 2.0 1.990 6.0 3.4 1.8 99.1 78.7 0.79 1.3 1.4 13.9 26.9 0.7 100.0 0 0 1 0 0 0 0 120 80 4 7 15.0 17.0 11.0
0 26 60.0 1.5 26.7 13 78 22 11.5 2 5 4.0 0 0 201.4 201.360 3.6 2.5 1.4 94.0 76.2 0.81 0.2 12.6 8.1 26.6 0.6 123.0 1 0 0 1 0 0 1 110 80 4 4 14.0 13.0 11.0
0 35 40.0 1.4 20.4 17 72 18 10.8 2 5 4.0 0 0 2.0 1.990 3.1 1.3 2.4 99.1 83.8 0.85 1.5 4.8 55.5 30.9 0.5 100.0 0 1 0 0 0 0 0 100 70 6 4 17.0 18.0 10.7
0 27 59.0 1.6 23.0 15 70 18 10.8 2 5 5.0 1 0 21977.3 16069.690 3.1 0.1 34.9 101.6 86.4 0.85 0.0 4.6 20.0 47.0 0.5 84.0 0 0 0 1 1 0 0 110 70 8 5 18.0 15.0 9.0
0 35 65.0 1.5 28.9 15 72 18 10.7 2 5 5.0 0 0 2.0 1.990 2.4 1.2 2.0 96.5 76.2 0.79 3.9 17.1 6.6 25.8 0.4 120.0 0 0 0 0 0 0 0 120 70 5 7 15.0 13.0 8.5
0 36 51.0 1.5 22.7 13 72 18 10.2 2 5 8.0 0 0 3.4 57.080 7.9 1.0 7.7 94.0 76.2 0.81 4.0 2.1 27.9 26.5 0.5 92.0 1 0 1 0 0 0 1 110 80 3 2 9.0 18.0 9.0
1 28 57.0 1.6 22.3 15 74 22 10.8 2 5 5.0 0 0 2.0 1.990 2.8 3.3 0.9 106.7 83.8 0.79 5.0 11.6 17.0 19.9 0.2 92.0 1 0 1 0 1 1 1 110 70 13 12 16.0 18.0 11.0
1 30 69.0 1.6 27.0 13 72 18 10.4 4 11 14.0 0 0 2.0 1.990 6.4 3.3 2.0 106.7 81.3 0.76 4.2 18.4 23.2 41.8 0.5 100.0 1 1 0 1 1 1 1 110 70 6 7 18.5 16.0 8.0
0 32 45.0 1.5 20.0 15 70 18 10.8 2 5 7.0 0 0 2.0 1.990 11.1 4.2 2.6 96.5 78.7 0.82 0.9 1.8 3.8 27.9 0.2 110.0 0 0 0 0 0 1 1 110 80 0 5 0.0 19.0 7.0
0 37 68.0 1.5 30.2 15 80 22 11.2 2 5 14.0 0 0 2.0 1.990 4.2 1.5 2.8 94.0 76.2 0.81 1.4 9.9 10.0 23.5 0.6 130.0 0 0 0 1 0 1 1 120 80 3 3 21.0 18.0 9.3
0 35 68.0 1.6 26.6 15 74 18 11.1 2 5 4.0 0 0 2.0 1.990 7.4 2.1 3.5 99.1 81.3 0.82 1.6 3.7 18.6 20.1 0.7 108.0 0 0 0 0 0 0 0 120 70 9 10 17.0 18.0 7.5
0 36 85.0 1.6 33.2 13 72 18 10.2 2 5 12.0 0 1 2.0 1.990 6.3 1.3 4.9 101.6 83.8 0.82 3.3 2.9 12.3 10.1 0.3 160.0 0 0 0 1 0 0 0 120 70 0 4 0.0 18.0 10.7
0 28 72.0 1.6 28.1 17 70 18 10.0 2 5 3.0 0 0 2.0 1.990 9.0 0.8 11.9 96.5 81.3 0.84 1.0 2.0 24.5 20.0 0.6 92.0 0 1 1 1 0 0 0 110 80 3 3 17.0 18.0 8.0
1 23 53.0 1.5 23.6 11 72 18 11.0 4 2 3.5 0 0 2.0 1.990 6.2 3.2 1.9 114.3 101.6 0.89 0.0 4.0 28.2 24.9 0.4 92.0 1 1 0 1 0 1 1 110 80 4 12 18.0 16.0 10.0
0 24 52.0 1.5 23.1 11 80 20 11.0 2 5 4.0 0 0 2.0 1.990 5.4 3.0 1.8 99.1 81.3 0.82 3.5 1.6 77.3 19.5 0.4 92.0 0 0 0 1 0 0 0 110 70 6 2 19.0 19.0 7.1
1 28 63.0 1.6 24.6 15 78 18 10.8 4 2 5.0 0 0 232.7 232.710 4.2 9.5 0.4 111.8 96.5 0.86 0.7 15.9 17.3 23.8 1.0 110.0 1 1 0 1 0 1 1 110 80 4 9 15.0 18.0 10.0
1 29 56.4 1.5 25.1 13 76 22 10.5 4 4 6.0 0 0 2.0 1.990 2.9 0.4 7.3 111.8 88.9 0.80 1.6 7.5 10.6 31.2 0.4 108.0 1 1 0 1 0 1 0 110 80 12 14 14.0 18.0 8.0
1 31 60.0 1.6 23.4 13 72 18 11.9 4 5 10.0 0 1 2.0 1.990 2.7 0.7 4.1 116.8 91.4 0.78 1.0 10.0 13.0 41.9 0.2 100.0 1 1 1 1 1 1 0 110 80 7 5 15.0 16.0 8.5
1 29 52.0 1.5 23.1 11 74 18 11.5 4 4 8.0 0 0 2.0 1.990 6.7 12.9 0.5 101.6 88.9 0.88 1.9 6.9 27.6 21.2 0.4 116.0 1 0 0 1 0 0 0 110 80 3 5 16.0 19.0 8.0
0 26 40.0 1.5 17.8 11 72 20 10.8 2 5 3.0 1 0 60.8 23.500 6.6 5.2 1.3 96.5 81.3 0.84 1.7 7.0 23.7 14.5 0.2 100.0 0 0 0 1 0 0 0 120 80 6 5 12.0 14.0 14.0
0 26 49.3 1.6 19.3 15 76 18 12.5 4 4 4.0 0 0 2.0 1.990 5.1 3.1 1.6 114.3 88.9 0.78 2.6 8.8 44.3 9.6 0.2 73.0 0 1 0 0 0 0 0 120 80 7 7 14.0 16.0 7.5
0 27 53.2 1.6 20.8 13 72 22 10.5 4 4 5.0 0 0 2.0 1.990 3.1 0.6 5.2 94.0 81.3 0.86 2.0 5.3 7.2 25.3 0.4 91.0 0 0 1 1 0 1 1 120 70 5 7 11.0 13.0 11.0
0 34 64.0 1.6 25.0 15 73 20 11.8 2 6 12.0 1 1 289.4 180.300 7.0 1.8 3.8 106.7 86.4 0.81 3.0 1.4 24.9 56.9 0.4 110.0 1 0 1 1 1 1 0 120 80 3 5 12.0 12.0 10.0
1 25 60.0 1.5 26.7 15 74 18 11.7 2 4 3.0 0 0 2.0 1.990 7.0 5.1 1.4 101.6 86.4 0.85 3.1 9.0 25.0 33.6 0.2 100.0 1 1 0 1 1 1 0 110 70 9 7 13.0 16.0 9.0
0 32 64.0 1.6 25.0 15 73 18 11.2 2 5 10.0 1 2 366.8 102.300 4.3 2.5 1.8 96.5 81.3 0.84 1.5 3.6 8.2 31.5 0.2 91.0 0 0 1 1 0 0 0 110 80 5 7 11.0 12.0 9.5
0 36 50.0 1.4 25.5 13 72 20 10.5 2 6 15.0 1 1 96.2 1.990 1.6 0.2 6.5 91.4 81.3 0.89 0.8 3.4 18.1 18.5 0.4 116.0 0 0 0 1 1 0 0 120 80 3 5 11.0 13.0 6.0
0 38 70.0 1.7 24.2 13 75 20 12.5 2 5 18.0 0 3 2.0 1.990 2.5 2.5 1.0 101.6 86.4 0.85 1.5 0.4 13.6 24.0 0.3 92.0 1 1 1 0 0 0 0 12 80 1 1 11.0 12.0 5.0
1 23 65.0 1.6 25.4 15 73 20 11.2 2 4 3.0 0 0 2.0 1.990 4.4 5.5 0.8 106.7 88.9 0.83 1.8 2.5 26.6 54.6 0.2 100.0 1 0 1 0 0 1 1 120 70 10 11 14.0 16.0 9.4
0 32 60.8 1.6 23.7 13 72 18 11.5 2 5 10.0 0 0 2.0 1.990 5.0 2.6 1.9 96.5 81.3 0.84 1.9 0.3 17.7 9.4 0.2 92.0 1 0 0 1 0 1 1 120 80 3 2 10.0 11.0 8.5
0 35 56.0 1.4 28.6 17 74 18 11.5 4 5 15.0 1 1 481.3 481.300 7.7 3.4 2.3 101.6 83.8 0.82 5.5 2.6 38.9 21.5 1.3 108.0 1 0 1 0 1 0 1 120 80 2 3 13.0 14.0 4.6
0 32 46.0 1.5 20.4 11 73 26 11.9 2 6 10.0 0 0 2.0 1.990 4.0 0.5 7.8 91.4 76.2 0.83 2.0 2.8 28.7 60.2 0.1 84.0 0 0 0 1 1 1 0 110 60 5 5 14.0 13.0 6.5
0 29 62.0 1.5 27.6 11 74 18 13.4 2 5 6.0 1 1 563.8 563.800 6.1 4.2 1.5 96.5 81.3 0.84 3.3 1.9 26.0 15.0 0.4 91.0 1 0 0 0 1 1 0 120 80 4 6 12.0 12.0 4.0
1 29 60.0 1.5 26.7 15 73 20 10.7 2 6 10.0 0 0 2.6 1.990 10.2 2.0 5.1 96.5 76.2 0.79 10.9 2.0 8.4 13.6 0.4 106.0 1 1 0 1 1 1 0 120 80 11 12 14.0 15.0 10.0
1 30 72.3 1.6 28.2 15 74 20 10.8 4 4 8.0 0 0 2.0 1.990 1.0 1.0 1.0 101.6 86.4 0.85 17.0 2.8 15.8 22.8 0.2 95.0 1 1 1 1 1 1 0 120 80 9 12 14.0 16.0 9.0
1 33 59.6 1.6 23.3 11 72 20 11.5 4 5 10.0 0 1 4.4 4.320 3.8 1.2 3.1 101.6 81.3 0.80 2.5 5.7 6.1 54.7 0.2 100.0 1 1 0 1 1 1 1 120 80 18 20 16.0 15.0 10.7
0 28 54.0 1.6 21.1 15 73 19 11.0 2 6 6.0 1 0 833.5 230.500 4.7 3.2 1.5 91.4 86.4 0.95 1.2 1.7 23.5 33.5 0.2 115.0 0 0 0 1 1 0 0 120 80 12 14 13.0 15.0 9.6
1 29 60.0 1.6 23.4 13 74 18 11.2 4 4 8.0 0 0 2.0 1.990 5.4 6.8 0.8 106.7 91.4 0.86 3.5 3.6 16.4 26.6 0.1 91.0 1 1 0 1 1 1 0 120 80 13 12 15.0 16.0 8.5
0 31 50.0 1.5 22.2 13 75 18 10.4 2 6 12.0 1 1 2036.2 155.300 1.9 0.3 5.7 91.4 76.2 0.83 2.7 3.6 15.0 48.0 0.9 91.0 0 0 1 0 1 0 1 120 80 3 2 14.0 12.0 5.5
1 35 73.5 1.6 28.7 15 72 20 10.1 4 5 10.0 0 2 3.9 3.888 3.1 0.4 7.1 106.7 86.4 0.81 0.8 3.5 16.6 31.2 0.4 108.0 1 1 1 1 0 1 0 120 80 15 16 13.0 14.0 9.5
0 37 60.0 1.5 26.7 11 72 18 11.7 2 6 15.0 1 1 698.2 523.600 9.1 5.1 1.8 96.5 81.3 0.84 2.9 2.0 10.8 17.1 1.0 91.0 1 0 1 0 0 1 0 120 80 1 1 10.0 11.0 5.0
1 27 58.0 1.5 25.8 15 74 18 11.1 4 4 5.0 0 0 2.0 1.990 6.2 7.3 0.8 91.4 76.2 0.83 2.6 8.0 30.0 6.5 0.3 84.0 1 0 1 1 1 1 0 120 80 12 13 15.0 16.0 9.0
1 28 78.0 1.6 30.5 16 73 18 10.5 2 4 8.0 0 0 3.7 1.650 4.5 2.8 1.6 106.7 96.5 0.90 4.0 9.0 20.8 10.2 0.3 108.0 1 1 1 1 0 1 0 110 70 15 14 14.0 12.0 8.0
1 24 89.0 1.7 30.8 17 74 18 11.2 4 4 2.5 0 0 2.0 1.990 5.9 9.9 0.6 101.6 88.9 0.88 1.7 11.5 42.4 25.7 0.3 91.0 1 1 0 1 1 1 0 120 80 11 8 15.0 14.0 8.0
1 37 88.0 1.7 30.4 12 74 18 11.9 2 5 15.0 0 1 3.8 3.830 9.2 3.2 2.9 104.1 96.5 0.93 1.7 10.2 3.6 49.0 0.6 350.0 1 1 1 1 0 1 0 110 80 12 14 14.0 16.0 11.0
0 35 58.0 1.6 22.7 11 72 20 12.0 2 6 13.0 1 0 2045.3 569.100 6.3 5.3 1.2 91.4 81.3 0.89 7.0 2.4 24.0 27.2 0.2 100.0 0 0 0 1 1 0 0 140 70 3 5 10.0 11.0 5.0
1 30 70.0 1.5 31.1 16 74 18 11.7 4 4 8.0 0 0 2.0 1.990 2.0 0.0 61.9 96.5 81.3 0.84 2.0 32.0 14.1 74.5 0.0 100.0 1 1 0 1 0 1 0 120 80 8 6 12.0 11.0 4.5
0 30 59.0 1.7 20.4 13 73 20 13.8 2 5 8.0 0 0 1.3 1.990 2.0 1.0 2.0 96.5 86.4 0.90 1.8 3.4 35.7 39.8 0.4 116.0 0 1 1 0 0 1 1 130 80 15 16 13.0 14.0 9.3
0 42 50.0 1.5 22.2 11 73 18 10.8 2 5 18.0 0 3 2.0 1.990 8.0 4.4 1.8 91.4 86.4 0.95 1.9 1.4 4.3 14.5 0.3 91.0 0 1 0 0 1 0 0 110 80 1 1 11.0 12.0 5.5
0 36 64.0 1.5 28.4 13 72 18 12.5 2 5 13.0 1 0 323.0 236.500 4.3 1.6 2.7 96.5 88.9 0.92 2.3 2.4 14.2 63.3 0.3 100.0 1 1 0 1 1 0 0 120 80 3 4 13.0 12.0 9.0
0 26 60.0 1.6 23.4 14 73 18 12.1 2 6 4.0 1 0 896.6 896.600 5.4 4.3 1.2 96.5 81.3 0.84 2.6 5.8 12.6 15.2 0.2 108.0 0 0 0 1 1 1 0 120 80 4 5 10.0 13.0 9.0
0 27 54.0 1.6 21.1 15 72 18 11.5 2 5 5.0 0 0 2.0 1.990 2.6 1.1 2.4 91.4 76.2 0.83 1.0 4.7 24.0 41.0 0.2 100.0 0 0 1 1 0 1 1 110 80 5 7 13.0 14.0 6.5
1 32 66.0 1.6 25.8 17 74 18 12.5 4 5 10.0 0 1 2.6 2.580 2.0 1.2 1.6 99.1 88.9 0.90 1.7 2.0 60.2 18.3 0.2 93.0 1 1 0 1 0 1 0 120 80 7 8 13.0 12.0 5.9
0 43 66.3 1.6 25.9 14 75 18 11.7 2 5 19.0 1 2 569.3 569.300 3.8 3.2 1.2 96.5 86.4 0.90 1.7 1.3 2.5 46.9 0.6 91.0 1 0 0 1 0 0 0 110 80 2 3 11.0 12.0 9.7
0 28 76.0 1.5 33.8 16 72 20 12.5 2 6 8.0 1 0 108.7 108.660 6.5 3.6 1.8 101.6 86.4 0.85 2.9 4.0 6.7 21.0 0.6 116.0 0 1 0 1 0 1 0 120 80 1 2 13.0 13.0 6.3
1 23 58.0 1.6 22.7 15 74 18 10.8 4 4 2.5 0 0 2.0 1.990 3.3 2.2 1.5 94.0 86.4 0.92 0.6 5.7 25.4 8.6 0.2 133.0 1 1 0 1 1 1 1 110 80 7 9 16.0 15.0 13.0
0 34 54.0 1.5 24.0 13 73 18 12.8 4 4 12.0 0 0 2.0 1.990 6.9 4.8 1.4 91.4 76.2 0.83 1.6 7.8 14.8 54.0 0.1 98.0 0 1 0 0 1 0 1 120 80 6 6 11.0 12.0 6.0
1 29 63.0 1.5 28.0 17 74 18 11.0 4 3 8.0 0 0 4.0 3.990 3.6 1.0 3.6 99.1 88.9 0.90 2.7 6.4 29.1 6014.7 0.2 123.0 1 1 1 1 1 1 0 120 70 14 16 16.0 17.0 9.0
0 30 67.0 1.7 23.2 12 72 22 12.0 4 5 10.0 1 1 455.8 121.800 5.7 2.4 2.4 94.0 81.3 0.86 2.4 5.8 10.8 28.7 0.2 108.0 0 1 0 0 1 0 1 110 80 2 4 10.0 13.0 6.0
0 30 55.0 1.7 19.0 11 72 20 13.8 2 5 8.0 0 0 2.0 1.990 5.6 4.4 1.3 86.4 81.3 0.94 10.2 1.7 20.6 29.1 0.9 91.0 0 0 0 1 1 0 0 120 80 2 2 10.0 11.0 7.0
0 29 50.0 1.5 22.2 13 74 18 10.8 4 3 6.0 1 0 122.6 122.580 5.5 7.1 0.8 91.4 86.4 0.95 2.8 8.8 25.7 57.1 0.4 102.0 0 1 0 0 1 1 0 120 80 5 6 13.0 12.0 7.0
1 35 60.0 1.5 26.7 13 72 20 13.2 4 4 14.0 0 1 2.0 1.990 22.0 3.4 6.5 96.5 88.9 0.92 2.4 6.7 16.3 5418.6 0.3 100.0 1 0 1 1 1 1 0 120 80 8 10 15.0 13.0 5.5
1 35 56.0 1.6 21.9 14 73 18 12.1 2 4 15.0 0 0 2.1 1.990 2.6 1.2 2.2 96.5 86.4 0.90 3.0 4.2 26.2 6.1 0.5 138.0 1 1 0 1 1 1 1 120 80 12 11 14.0 14.0 5.6
0 28 70.0 1.6 27.3 15 72 18 12.0 2 5 6.0 0 0 2.0 1.990 5.3 2.7 2.0 99.1 88.9 0.90 2.5 1.9 10.9 61.6 0.8 90.0 1 1 0 1 0 1 0 110 80 5 7 12.0 14.0 9.0
0 32 49.0 1.5 21.8 11 72 20 11.2 2 5 12.0 1 1 689.1 355.280 7.1 3.8 1.9 88.9 81.3 0.91 1.4 2.0 23.2 47.4 0.2 92.0 0 0 0 1 1 1 0 110 70 6 4 10.0 14.0 6.0
1 25 58.0 1.6 22.7 15 74 18 11.9 4 3 3.0 0 0 2.0 1.990 3.8 3.6 1.1 96.5 91.4 0.95 3.9 7.2 30.8 55.6 0.2 90.0 1 1 1 1 0 1 0 110 80 12 12 14.0 17.0 10.5
0 30 47.0 1.6 18.4 15 73 18 11.2 2 6 10.0 0 0 3.4 1.990 9.0 3.1 2.9 91.4 81.3 0.89 1.7 1.0 28.7 22.0 0.2 91.0 0 0 0 1 1 1 1 120 8 2 1 11.0 14.0 6.0
0 28 56.0 1.6 21.9 13 72 20 10.8 2 5 8.0 1 0 122.3 122.300 5.2 5.0 1.0 94.0 88.9 0.95 2.7 1.9 14.0 9.0 0.1 100.0 1 0 0 1 0 1 0 120 80 2 4 12.0 12.0 9.8
0 28 80.0 1.6 31.2 15 74 20 11.8 2 6 6.0 1 0 596.2 596.200 3.1 4.1 0.8 101.6 91.4 0.90 1.7 2.3 10.3 62.0 0.9 116.0 1 1 1 1 1 1 0 120 80 3 2 12.0 14.0 9.9
1 23 49.0 1.4 25.0 14 73 18 13.2 4 4 4.0 0 0 2.0 1.990 4.1 2.6 1.6 91.4 86.4 0.95 2.0 5.6 10.2 15.4 0.8 108.0 1 1 0 1 0 1 0 110 80 14 16 17.0 15.0 10.6
0 30 55.0 1.6 21.5 12 72 18 11.2 2 6 10.0 0 0 4.6 2.800 6.1 3.8 1.6 94.0 88.9 0.95 2.3 3.0 30.7 48.6 0.4 91.0 0 0 0 1 1 1 1 120 70 5 6 12.0 15.0 8.5
1 28 62.0 1.6 24.2 15 73 18 12.0 4 5 7.0 0 0 2.1 1.990 3.4 0.8 4.5 99.1 88.9 0.90 0.8 5.2 23.1 43.7 0.3 140.0 1 1 1 0 1 1 1 110 80 15 18 15.0 19.0 8.0
1 24 83.0 1.6 32.4 15 75 18 12.6 4 4 2.5 0 0 2.5 1.990 3.3 0.8 4.2 106.7 96.5 0.90 2.6 2.4 30.3 63.1 0.3 110.0 1 1 1 1 1 1 0 120 80 16 15 12.0 14.0 5.5
0 32 60.0 1.4 30.6 15 73 18 11.2 4 5 12.0 1 2 2054.3 588.700 2.5 1.4 1.7 94.0 86.4 0.92 3.6 7.0 11.8 87.2 0.2 108.0 1 0 0 1 1 0 0 120 80 3 4 14.0 12.0 7.5
0 27 51.0 1.5 22.7 13 72 18 12.0 2 6 7.0 1 0 147.6 147.600 5.8 3.6 1.6 91.4 83.8 0.92 1.2 3.2 25.0 90.0 0.7 84.0 0 0 0 1 1 0 0 110 80 5 7 12.0 13.0 6.5
1 35 62.0 1.5 27.6 17 74 20 13.4 4 4 12.0 0 1 2.0 1.990 1.6 0.6 2.6 96.5 88.9 0.92 1.6 5.6 12.4 46.6 1.1 98.0 1 1 0 1 1 1 1 120 80 8 10 13.0 15.0 5.5
1 32 54.0 1.4 27.6 11 72 18 10.8 4 5 10.0 0 0 2.2 1.990 3.1 0.4 7.4 94.0 86.4 0.92 1.4 4.6 32.0 21.2 0.4 102.0 1 0 1 1 0 1 0 120 80 12 15 15.0 18.0 6.1
1 39 55.0 1.5 24.4 15 78 20 10.7 4 4 13.0 0 0 12.4 12.370 5.4 2.0 2.8 86.4 81.3 0.94 1.0 0.4 21.9 18.6 0.5 94.0 0 1 1 0 0 1 0 120 80 12 11 17.0 14.0 15.0
0 27 52.0 1.5 23.1 11 82 20 11.1 2 5 5.0 0 0 2.0 1.990 5.0 2.2 2.2 106.7 96.5 0.90 1.9 16.9 9.1 23.6 0.2 93.0 0 0 0 1 1 0 1 120 80 1 1 17.0 16.0 9.4
1 26 70.0 1.5 31.1 15 72 20 12.0 4 5 3.0 1 0 144.6 144.630 3.6 8.3 0.4 91.4 86.4 0.95 2.3 26.4 29.7 22.4 0.4 93.0 1 1 0 0 1 1 0 110 80 8 7 17.0 14.0 6.0
0 29 63.0 1.5 28.0 11 72 18 13.2 2 6 11.0 1 4 25000.0 475.040 2.0 1.6 1.2 96.5 86.4 0.90 1.8 6.0 22.0 22.7 0.5 95.0 1 0 0 1 1 1 0 110 80 6 7 16.0 14.0 5.0
0 41 71.0 1.6 27.7 15 74 20 11.0 4 3 16.0 0 1 2.0 1.990 4.7 3.6 1.3 101.6 91.4 0.90 1.1 9.1 22.6 31.6 0.2 85.0 1 0 0 1 1 1 1 120 80 9 7 14.0 14.0 10.0
1 36 66.0 1.6 25.8 15 72 20 11.0 2 5 13.0 1 2 515.5 515.530 1.6 0.2 9.6 91.4 86.4 0.95 1.9 6.6 25.0 0.0 0.2 93.0 1 1 1 1 1 1 1 120 80 14 6 15.0 13.0 9.7
1 34 72.0 1.6 28.1 13 72 18 11.0 4 4 13.0 0 1 2.0 99.690 4.8 2.7 1.8 96.5 88.9 0.92 2.3 22.0 12.5 22.7 0.2 93.0 1 1 0 1 1 1 0 110 80 9 12 15.0 13.0 10.0
0 26 47.8 1.6 18.7 13 72 20 10.2 2 6 7.0 1 0 70.4 70.420 6.3 4.0 1.6 86.4 81.3 0.94 2.1 1.9 11.8 23.8 0.2 120.0 0 0 0 1 1 0 0 120 80 6 6 17.0 16.0 10.2
0 27 76.8 1.6 30.0 13 78 20 11.1 4 4 1.0 0 0 342.9 342.910 7.3 5.3 1.4 94.0 88.9 0.95 6.1 4.3 23.5 21.8 0.2 93.0 1 0 0 1 1 1 0 120 80 3 2 14.0 15.0 14.0
0 39 61.0 1.5 27.1 11 72 18 11.0 2 4 20.0 0 3 2.0 1.990 8.9 1.5 6.0 91.4 86.4 0.95 2.3 0.4 14.2 25.5 0.2 93.0 0 0 0 1 0 0 1 120 80 1 1 16.0 12.0 10.0
1 31 31.0 1.6 12.1 15 80 20 12.0 4 3 3.0 0 0 2.0 1.990 4.0 6.6 0.6 81.3 71.1 0.87 1.3 17.6 13.9 19.6 0.2 133.0 0 1 1 1 0 1 0 120 70 7 6 14.0 16.0 10.0
1 27 65.0 1.6 25.4 17 78 20 11.1 2 5 5.0 1 0 148.5 148.520 5.0 1.8 2.8 88.9 81.3 0.91 2.2 1.1 12.1 25.7 0.3 93.0 1 1 1 0 1 1 0 120 70 14 10 16.0 15.0 8.0
0 30 62.0 1.7 21.5 11 18 20 12.0 2 5 5.0 0 0 2.0 1.990 7.0 4.1 1.7 96.5 86.4 0.90 1.5 7.8 19.6 31.8 0.2 95.0 0 0 0 1 0 1 1 120 70 4 3 14.0 18.0 8.7
0 36 62.0 1.5 27.6 11 70 18 10.0 2 6 11.0 0 0 2.0 1.990 4.6 0.6 7.9 96.5 91.4 0.95 2.7 2.9 45.5 18.7 0.3 106.0 1 0 1 1 0 1 0 110 80 6 6 17.0 16.0 7.7
0 32 52.0 1.5 23.1 13 70 18 9.4 2 5 12.0 0 0 272.8 272.780 4.3 2.1 2.0 88.9 83.8 0.94 2.1 7.7 40.5 41.0 0.2 93.0 0 0 0 1 0 0 1 110 80 6 7 16.0 15.0 12.0
0 35 61.0 1.6 23.8 13 72 18 10.5 2 6 13.0 1 1 355.5 355.510 3.7 2.7 1.4 91.4 86.4 0.95 3.0 9.7 20.4 15.0 0.2 93.0 0 1 0 0 1 1 1 120 80 12 13 14.0 17.0 13.0
1 29 75.0 1.6 29.3 15 72 18 10.7 2 6 3.0 0 0 2.0 1.990 3.3 1.6 2.1 94.0 88.9 0.95 2.4 0.2 34.7 10.1 0.4 85.0 1 1 1 1 1 1 0 110 80 1 3 11.0 12.0 6.0
0 31 61.0 1.5 27.1 13 74 20 12.3 2 4 10.0 0 2 2.0 1.990 7.8 1.5 5.1 101.6 96.5 0.95 2.1 2.5 10.7 10.4 0.2 83.0 1 0 0 1 1 1 1 120 80 2 4 16.0 12.0 10.8
0 28 74.3 1.5 33.0 13 72 18 10.7 2 5 6.0 0 3 2.0 1.990 3.7 4.6 0.8 96.5 91.4 0.95 1.7 12.0 46.2 10.8 0.8 85.0 1 0 1 1 0 1 0 110 80 6 5 14.0 13.0 8.5
1 40 71.0 1.5 31.6 15 72 18 13.2 2 5 7.0 0 0 2.0 1.990 2.8 1.0 2.8 96.5 88.9 0.92 3.8 1.4 29.0 20.8 0.4 160.0 1 1 0 1 0 1 0 120 80 4 2 17.0 10.0 12.0
0 22 78.0 1.5 34.7 15 72 18 12.4 2 5 9.0 0 1 150.9 150.910 5.2 2.2 2.4 101.6 91.4 0.90 1.2 16.7 10.0 20.8 0.3 77.0 1 0 0 0 1 0 0 120 80 1 1 13.0 10.0 8.8
1 35 55.0 1.6 21.5 13 72 18 12.4 4 4 7.0 0 0 452.0 1.990 2.6 1.0 2.5 88.9 81.3 0.91 2.1 13.6 28.9 32.2 0.3 86.0 0 1 1 0 1 1 0 120 80 16 14 15.0 18.0 11.5
0 31 50.0 1.5 22.2 15 72 18 10.0 2 6 4.5 0 1 392.7 1.990 5.4 8.2 0.7 86.4 76.2 0.88 25.9 16.8 14.4 23.9 0.2 92.0 0 0 0 0 1 1 1 120 80 7 5 17.0 15.0 9.0
0 27 45.0 1.5 20.0 13 72 18 11.5 2 6 4.0 0 0 391.5 391.460 1.9 0.1 18.5 81.3 76.2 0.94 3.8 3.5 29.3 22.3 0.2 73.0 0 1 1 0 1 0 1 120 80 1 3 14.0 16.0 8.5
0 28 58.1 1.5 25.8 13 72 18 12.4 2 5 4.0 0 0 418.8 1.990 7.5 1.5 5.1 91.4 86.4 0.95 0.7 1.3 27.7 31.1 0.2 86.0 0 1 0 0 0 1 1 110 70 0 5 17.0 18.0 4.8
0 33 54.0 1.5 24.0 15 72 18 12.8 2 3 8.0 0 1 2.0 1.990 3.2 0.6 5.6 96.5 88.9 0.92 11.0 3.1 45.0 28.7 0.2 80.0 0 0 0 0 1 1 1 120 80 5 5 18.0 20.0 7.0
1 25 42.0 1.5 18.7 14 72 18 12.5 4 3 4.0 0 0 2.0 1.990 6.6 3.1 2.1 83.8 76.2 0.91 0.8 1.2 12.8 30.2 0.7 100.0 1 0 0 1 1 0 0 120 80 6 5 15.0 17.0 9.0
0 32 66.0 1.6 25.8 15 72 18 11.1 4 4 13.0 0 0 464.1 464.120 3.8 1.5 2.5 86.4 78.7 0.91 0.9 7.7 34.9 16.7 0.2 98.0 0 0 1 0 1 1 1 120 80 5 5 17.0 19.0 9.0
0 20 56.0 1.6 21.9 13 72 18 12.4 2 5 1.5 1 0 389.3 41.770 5.9 3.1 1.9 88.9 83.8 0.94 0.6 7.3 10.6 22.0 0.2 86.0 0 0 0 0 1 0 1 120 80 10 5 15.0 18.0 8.0
1 24 53.6 1.7 18.5 15 72 18 10.7 2 5 3.0 1 0 1390.6 1390.580 3.9 1.0 3.9 86.4 81.3 0.94 5.0 7.2 40.3 30.7 0.3 86.0 0 1 0 1 1 0 0 110 80 12 9 15.0 18.0 8.0
0 23 60.0 1.6 23.4 17 72 18 11.5 2 6 2.0 0 0 2.0 1.990 6.1 2.1 2.8 96.5 88.9 0.92 1.4 3.3 29.1 26.0 0.2 93.0 0 0 0 1 1 0 0 110 80 6 4 16.0 19.0 14.0
0 37 65.0 1.6 25.4 13 72 20 12.6 2 5 10.0 0 0 2.0 1.990 1.3 1.0 1.3 91.4 83.8 0.92 2.0 2.7 15.2 35.8 0.4 113.0 1 1 0 1 0 1 0 120 80 6 5 15.0 18.0 9.0
1 29 63.0 1.5 28.0 15 72 18 11.5 2 6 2.0 0 0 213.8 213.830 3.2 1.0 3.2 101.6 94.0 0.93 2.5 2.1 29.6 31.6 0.2 92.0 1 1 1 1 0 1 0 120 80 4 7 10.0 15.0 9.2
1 39 104.0 1.6 40.6 15 72 18 12.0 2 6 11.0 0 1 2.0 1.990 6.4 2.3 2.8 116.8 111.8 0.96 5.0 4.1 13.4 32.6 0.2 106.0 1 1 1 1 1 1 0 120 80 9 7 16.0 13.0 9.6
1 23 57.0 1.5 25.3 15 73 18 12.0 2 4 3.0 1 0 353.6 45.900 3.2 1.3 2.5 96.5 88.9 0.92 0.9 6.2 23.1 30.1 0.2 106.0 0 0 1 1 1 1 1 120 80 8 10 17.0 18.0 9.0
0 26 54.0 1.6 21.1 15 74 20 10.0 2 5 2.0 0 0 30.6 18.360 4.1 2.0 2.0 91.4 86.4 0.95 0.3 14.6 13.3 12.2 0.2 93.0 0 0 0 1 0 1 1 120 70 1 2 14.0 16.0 8.0
1 24 79.0 1.7 27.3 16 72 18 10.2 2 5 4.0 0 0 154.5 154.480 3.1 1.9 1.6 106.7 96.5 0.90 4.1 4.7 31.4 24.0 0.2 86.0 1 1 1 0 1 1 0 110 80 10 15 18.0 19.0 10.3
1 33 65.0 1.5 28.9 17 72 18 12.0 4 5 1.0 0 0 2.0 1.990 5.4 7.8 0.7 96.5 91.4 0.95 1.3 1.2 111.7 12.8 0.2 146.0 1 1 1 0 1 1 0 110 70 7 10 16.0 19.0 12.0
0 29 52.0 1.6 20.3 15 72 18 14.2 2 4 1.5 0 1 2.0 1.990 4.7 1.6 2.9 88.9 83.8 0.94 0.3 1.0 23.8 39.7 0.2 94.0 0 0 0 1 0 0 0 120 70 4 6 18.0 17.0 11.0
1 45 85.0 1.5 37.8 15 72 18 12.0 4 4 18.0 0 0 50.4 1.990 65.4 0.2 327.0 111.8 101.6 0.91 3.0 11.1 21.7 18.7 0.2 86.0 1 1 1 1 1 1 0 120 80 10 15 15.0 17.0 8.3
0 30 63.8 1.5 28.4 11 72 18 11.5 4 3 7.0 0 0 2.0 1.990 7.4 3.5 2.1 101.6 94.0 0.93 2.9 1.9 15.3 21.2 0.2 80.0 0 0 0 0 0 1 1 120 70 0 1 17.0 15.0 13.0
1 47 62.7 1.5 27.9 15 72 18 10.0 4 4 30.0 1 2 25000.0 25000.000 1.9 0.2 7.5 96.5 91.4 0.95 2.5 6.2 31.5 12.1 0.2 92.0 1 1 1 1 0 0 0 110 80 14 11 16.0 19.0 9.3
0 36 46.0 1.5 20.4 15 72 18 13.3 2 5 18.0 0 0 2.0 1.990 1.6 0.3 6.1 86.4 81.3 0.94 4.9 1.9 26.1 26.3 0.2 105.0 0 0 0 0 1 0 0 110 80 1 3 18.0 14.0 6.0
0 28 52.0 1.6 20.3 13 72 16 11.2 2 5 11.0 0 1 41.2 1.990 8.9 3.5 2.5 86.4 81.3 0.94 1.8 1.5 19.2 29.5 0.2 100.0 0 0 0 0 1 0 0 120 70 2 3 15.0 18.0 8.0
0 36 32.0 1.5 14.2 15 72 20 12.5 2 6 15.0 0 0 2.0 638.520 1.3 0.9 1.4 81.3 76.2 0.94 0.9 2.9 5.3 31.5 0.4 80.0 0 0 0 1 1 0 0 120 80 5 7 16.0 17.0 8.2
0 33 35.0 1.5 15.6 13 74 20 12.0 2 5 10.0 0 1 413.6 1.990 3.9 0.5 8.3 76.2 71.1 0.93 1.4 2.2 29.9 27.6 0.2 107.0 0 0 0 1 1 0 0 120 70 4 1 18.5 19.0 8.5
0 27 48.0 1.6 18.7 15 72 18 12.5 2 6 6.5 0 0 2.0 1.990 1.8 0.1 17.6 83.8 76.2 0.91 1.2 6.8 22.5 13.7 0.4 72.0 0 0 0 1 0 1 0 110 80 2 5 17.0 18.0 9.0
0 43 33.0 1.5 14.7 15 72 18 11.1 2 6 6.0 0 0 221.3 1.990 12.3 2.5 4.9 76.2 71.1 0.93 1.2 0.8 11.0 22.4 0.2 100.0 0 0 0 1 1 1 1 120 80 9 8 18.0 19.0 10.0
1 31 50.0 1.6 19.5 11 74 20 12.5 2 5 4.0 0 0 227.4 1.990 2.1 0.4 5.7 86.4 81.3 0.94 5.0 7.2 27.0 32.1 0.2 100.0 0 1 1 1 0 1 0 120 80 10 15 17.0 18.0 8.0
0 38 34.0 1.5 15.1 13 72 18 14.8 2 5 8.0 0 1 2.0 1.990 3.0 0.5 6.0 81.3 76.2 0.94 2.6 4.2 30.2 24.6 0.4 100.0 0 0 0 1 0 1 0 130 80 1 3 15.0 17.0 8.4
0 32 35.0 1.5 15.6 11 70 18 11.5 2 4 4.0 0 0 643.4 1.990 1.7 0.5 3.7 81.3 76.2 0.94 2.8 6.2 19.3 20.9 0.2 72.0 0 0 0 0 0 1 0 110 80 1 0 19.0 20.0 10.0
0 38 53.5 1.6 20.9 13 72 18 12.5 2 5 14.0 0 0 2.0 1.990 1.8 0.5 3.7 88.9 83.8 0.94 1.6 0.9 45.8 23.5 0.2 87.0 0 0 0 0 0 0 0 110 70 4 1 17.0 13.0 6.5
0 37 45.0 1.6 17.6 17 74 20 12.1 2 5 10.0 0 0 3.2 4.760 10.4 4.1 2.5 88.9 81.3 0.91 1.7 16.8 33.6 43.9 0.2 100.0 0 0 0 1 1 0 0 120 80 5 5 19.0 18.0 8.7
0 34 35.0 1.6 13.7 13 74 20 12.4 2 4 8.0 0 0 165.6 1.990 4.9 2.3 2.1 81.3 73.7 0.91 3.4 16.9 23.6 16.4 0.3 88.0 0 0 0 1 1 0 0 110 70 10 7 18.0 18.0 9.0
0 40 32.0 1.5 14.2 15 72 20 10.0 2 3 2.0 0 1 2.0 1.990 5.0 1.6 3.1 76.2 71.1 0.93 5.0 8.5 23.3 27.7 0.3 88.0 0 0 0 0 0 1 1 120 80 6 5 18.0 17.0 8.0
0 25 64.4 1.6 25.2 11 72 18 11.5 2 5 4.0 0 0 92.9 1.990 6.9 2.9 2.4 86.4 81.3 0.94 4.0 3.9 21.9 30.8 0.4 84.0 0 0 0 1 1 0 0 110 80 3 2 19.0 18.0 5.6
1 28 58.9 1.6 23.0 11 72 20 12.4 2 6 1.0 0 0 117.1 18.130 7.3 3.6 2.0 91.4 86.4 0.95 1.7 66.0 15.4 30.2 0.4 70.0 0 1 1 1 0 1 1 120 80 7 10 19.0 19.0 10.0
0 25 53.5 1.5 23.8 11 72 18 12.4 4 3 3.0 0 0 100.6 1.990 5.5 0.9 6.0 81.3 76.2 0.94 3.2 26.8 33.1 21.4 0.2 70.0 0 0 0 1 0 0 0 110 80 6 5 17.0 19.0 7.0
1 26 59.0 1.5 26.2 17 70 18 10.5 2 4 3.0 0 0 2.0 1.990 1.0 0.1 10.0 88.9 83.8 0.94 0.6 1.1 35.1 20.7 0.2 100.0 1 1 1 1 1 1 0 110 80 9 4 20.0 16.0 13.0
0 30 55.1 1.7 19.1 11 72 18 12.0 2 5 8.5 0 0 127.0 1.990 7.9 2.2 3.5 86.4 81.3 0.94 1.5 4.1 24.0 22.1 0.2 83.0 1 1 1 1 1 1 0 110 80 7 6 19.0 18.0 8.0
0 26 55.7 1.6 21.8 17 72 18 11.0 2 6 1.0 0 0 164.9 1.990 6.1 3.2 1.9 91.4 83.8 0.92 4.8 3.2 15.6 31.6 0.2 84.0 0 0 0 0 1 0 1 110 70 2 3 18.0 18.0 8.7
0 43 40.0 1.5 17.8 15 72 18 11.5 2 6 25.0 0 0 2.0 1.990 15.0 7.1 2.1 76.2 71.1 0.93 6.1 0.2 24.8 24.5 0.3 100.0 0 0 0 0 1 0 1 120 70 4 4 17.0 18.0 8.3
0 30 52.0 1.5 23.1 13 78 20 11.1 2 4 6.0 1 0 785.4 1.990 6.3 6.3 1.0 81.3 76.2 0.94 2.6 8.1 38.2 26.3 0.2 112.0 0 0 0 1 1 0 1 120 80 8 7 18.0 16.0 7.0
0 30 58.9 1.5 26.2 13 72 18 12.0 2 5 4.0 0 0 2.0 1.990 5.6 3.9 1.4 88.9 83.8 0.94 3.0 0.6 128.2 30.3 0.3 88.0 0 0 0 0 0 0 1 120 80 4 6 17.0 17.0 10.3
1 27 47.0 1.5 20.9 15 74 20 10.7 2 5 8.0 0 0 355.1 1.990 6.8 4.6 1.5 83.8 71.1 0.85 3.5 5.3 14.7 26.1 0.2 90.0 0 0 0 0 0 0 1 120 70 12 15 16.0 18.0 8.7
1 36 65.7 1.5 29.2 15 72 20 11.0 4 4 8.0 0 1 89.3 89.340 7.5 6.6 1.1 94.0 88.9 0.95 5.0 1.8 8.0 19.9 0.2 92.0 1 0 0 1 1 1 0 120 80 6 5 18.0 16.0 8.8
1 29 66.0 1.5 29.3 15 72 18 12.0 4 5 9.0 0 0 2.0 1.990 2.6 2.4 1.1 91.4 86.4 0.95 2.9 7.3 12.9 19.0 0.4 92.0 0 1 1 1 0 0 1 110 70 1 1 12.0 14.0 9.5
1 45 50.0 1.5 22.2 17 72 18 10.2 4 4 11.0 0 2 412.9 366.040 2.2 2.0 1.1 76.2 68.6 0.90 22.6 6.5 16.8 10.5 0.5 138.0 1 1 1 1 1 1 0 120 80 4 8 15.0 18.0 7.2
0 26 54.0 1.5 24.0 11 72 18 12.0 4 3 5.0 0 0 184.8 1.990 5.8 3.9 1.5 91.4 86.4 0.95 2.4 21.0 29.7 20.1 0.5 125.0 1 1 1 1 0 1 0 120 70 3 2 15.0 13.0 9.0
0 31 65.0 1.6 25.4 15 72 18 12.4 2 5 4.0 0 0 2.0 1.990 3.0 0.3 11.3 96.5 91.4 0.95 3.2 0.9 17.2 25.5 0.7 92.0 0 1 0 1 1 0 1 120 70 3 3 18.0 15.0 8.9
0 31 51.0 1.6 19.9 17 70 18 11.0 4 5 5.0 0 0 2.0 1.990 60.4 10.0 6.0 83.8 76.2 0.91 1.0 15.3 20.5 25.6 0.3 92.0 0 0 0 0 1 0 1 110 80 4 5 16.0 19.0 7.0
1 38 62.0 1.5 27.6 15 74 20 10.5 4 4 5.0 1 0 454.3 14.340 8.2 3.3 2.5 88.9 83.8 0.94 7.0 10.6 22.9 16.7 0.5 92.0 1 0 0 1 1 1 0 120 80 8 5 15.0 17.0 9.8
0 32 68.0 1.6 26.6 17 72 18 10.5 2 6 13.0 0 0 3.1 75.620 2.4 0.3 8.1 94.0 88.9 0.95 5.0 4.2 38.5 24.0 0.7 100.0 1 0 0 0 1 1 1 110 80 3 5 17.0 16.0 9.8
0 32 46.0 1.5 20.4 15 72 20 11.5 2 6 8.0 0 0 2.0 1.990 4.8 4.5 1.1 81.3 76.2 0.94 2.0 4.7 96.6 22.6 0.5 112.0 0 0 0 1 1 0 0 120 80 1 1 11.0 16.0 11.0
0 38 52.0 1.6 20.3 17 72 18 10.8 2 5 10.0 0 0 227.6 4.960 3.8 0.3 11.0 83.8 76.2 0.91 1.3 4.8 17.1 20.1 0.3 110.0 0 0 0 1 0 0 1 120 80 1 2 20.0 17.0 7.2
0 26 67.0 1.5 29.8 11 72 18 13.2 2 5 5.0 0 1 2.0 1.990 7.8 2.7 2.9 91.4 86.4 0.95 3.7 1.7 24.0 17.1 0.2 122.0 1 0 0 1 0 1 0 120 70 2 1 16.0 14.0 7.8
1 29 53.0 1.5 23.6 17 78 22 11.0 2 5 2.5 0 0 53.1 1.990 6.1 2.1 2.8 86.4 81.3 0.94 2.4 5.4 12.3 18.0 0.2 100.0 0 1 1 0 1 0 0 110 80 10 7 18.0 14.0 9.0
0 35 66.0 1.6 25.8 15 78 22 10.5 4 3 4.0 0 2 2.0 1.990 6.5 3.2 2.0 94.0 88.9 0.95 4.6 1.2 18.1 27.2 0.3 100.0 1 0 0 0 0 1 0 110 80 1 0 15.0 18.0 7.7
0 27 65.0 1.6 25.4 15 72 18 10.7 2 5 3.5 0 1 213.2 1.990 5.5 5.0 1.1 96.5 91.4 0.95 4.3 5.4 41.0 21.4 0.2 92.0 1 1 1 1 1 0 0 110 80 5 3 16.0 17.0 8.0
1 27 83.0 1.6 32.4 13 70 18 11.0 2 6 2.0 0 0 2.0 1.990 4.6 2.5 1.9 99.1 91.4 0.92 6.5 17.5 12.9 26.6 0.5 92.0 0 0 0 1 1 0 0 110 80 1 1 14.0 15.0 8.5
0 35 53.0 1.6 20.7 17 74 20 12.4 2 6 11.0 0 1 2.0 1.990 9.0 4.9 1.8 88.9 83.8 0.94 1.3 6.0 17.0 30.5 0.4 110.0 1 0 0 0 1 1 0 120 80 1 3 16.0 17.0 9.0
0 30 68.0 1.6 26.6 15 72 18 10.5 2 4 10.0 0 0 2.0 1.990 1.8 0.2 8.8 94.0 88.9 0.95 0.9 2.0 43.6 35.8 0.4 92.0 0 1 1 1 0 0 1 120 70 2 2 12.0 16.0 10.5
1 27 36.0 1.5 16.0 13 70 18 11.4 2 5 6.5 1 0 1134.4 1134.400 4.2 5.1 0.8 76.2 71.1 0.93 2.1 21.8 19.3 23.3 0.4 79.0 1 1 1 0 1 1 0 110 80 11 12 13.0 15.0 5.5
1 28 55.5 1.4 28.3 13 72 20 11.8 2 5 6.5 1 2 786.0 785.950 4.0 3.0 1.4 96.5 88.9 0.92 0.9 18.5 7.5 20.4 0.6 95.0 1 1 1 0 1 1 0 120 80 13 20 14.0 16.0 9.6
1 30 57.0 1.6 22.3 13 72 18 12.0 2 5 10.0 0 0 291.4 1.990 5.5 3.0 1.8 88.9 83.8 0.94 6.5 12.4 39.6 23.9 0.9 106.0 0 0 0 0 0 1 1 110 70 9 10 18.0 19.0 8.0
0 31 50.0 1.6 19.5 15 13 18 11.0 2 4 12.0 0 0 20.8 1.990 6.7 5.1 1.3 83.8 76.2 0.91 3.9 10.8 14.0 24.9 0.4 100.0 0 0 1 0 1 0 0 110 70 8 5 17.0 15.0 8.5
0 20 52.0 1.5 23.1 15 72 18 10.5 2 6 3.0 0 0 2.0 1.990 4.9 3.6 1.4 86.4 81.3 0.94 2.9 4.5 20.2 27.0 0.3 88.0 0 0 0 1 1 1 0 110 70 7 9 13.0 17.0 9.6
0 34 67.0 1.5 29.8 15 74 20 11.5 2 6 13.0 0 1 2.0 1.990 5.6 3.0 1.8 94.0 88.9 0.95 3.7 4.2 14.9 13.9 0.7 95.0 1 1 1 1 1 1 0 120 70 1 0 16.0 12.0 7.7
0 26 50.0 1.5 22.2 11 72 20 11.0 4 5 2.0 0 0 2.0 1.990 5.7 4.5 1.3 81.3 71.1 0.87 1.2 1.2 38.7 23.2 0.3 92.0 0 0 1 0 0 1 1 120 80 1 0 15.0 18.0 11.0
1 40 85.0 1.7 29.4 13 72 18 10.5 2 5 13.0 0 1 2.0 1.990 4.4 1.6 2.7 96.5 91.4 0.95 3.7 10.8 29.8 18.3 0.3 100.0 1 0 0 0 1 1 0 110 80 2 1 18.0 15.0 7.0
0 28 53.0 1.5 23.6 11 72 18 11.2 2 4 7.5 0 1 110.8 1.990 5.7 2.8 2.0 83.8 76.2 0.91 4.7 10.0 22.1 30.3 0.4 100.0 1 1 1 1 0 1 0 110 80 7 5 16.0 13.0 9.0
0 26 64.0 1.5 28.4 17 72 18 12.0 4 3 3.0 0 0 2.0 1.990 5.1 8.2 0.6 86.4 78.7 0.91 1.1 18.7 31.8 30.0 0.5 135.0 0 0 0 1 0 0 1 120 80 3 4 16.0 14.0 9.5
1 25 57.0 1.5 25.3 15 72 20 10.0 2 5 1.0 0 0 2.0 1.900 2.5 1.4 1.8 76.2 71.1 0.93 3.0 18.0 35.0 31.2 0.6 80.0 1 1 1 1 1 0 0 110 80 10 12 15.0 14.0 9.0
0 35 52.0 1.5 23.1 13 70 18 10.7 2 5 7.0 0 0 2.0 1.990 5.0 2.2 2.3 86.4 81.3 0.94 0.8 0.3 16.4 25.0 0.3 94.0 0 0 0 0 1 0 1 110 80 3 2 17.0 15.0 12.0
0 37 56.0 1.5 24.9 13 74 20 11.7 2 5 9.0 0 0 42.0 1.990 2.9 0.3 8.3 88.9 83.8 0.94 16.0 3.7 2.2 38.6 0.3 100.0 0 0 0 0 1 0 1 120 70 4 5 17.0 16.0 5.6
1 41 40.0 1.6 15.6 13 72 16 11.0 2 5 7.0 0 0 2.0 1.990 3.7 0.8 4.4 86.4 81.3 0.94 1.1 1.0 7.5 33.1 0.2 82.0 0 1 1 0 1 0 0 110 70 8 12 11.0 11.5 5.5
0 24 68.0 1.6 26.6 15 72 16 10.8 2 6 3.0 1 0 229.9 229.860 3.3 2.0 1.7 99.1 91.4 0.92 2.0 3.0 10.6 22.5 0.2 80.0 1 0 0 0 1 1 0 120 80 2 4 12.5 12.0 8.0
1 33 52.0 1.5 23.1 11 72 18 11.0 2 5 8.0 0 1 2.0 1.990 5.8 4.5 1.3 96.5 81.3 0.84 3.7 1.0 33.7 31.6 0.2 92.0 0 1 1 1 1 1 0 110 70 6 9 13.0 13.0 6.0
1 24 52.0 1.6 20.3 15 72 18 11.0 4 3 4.0 0 0 2.0 1.990 3.3 1.7 2.0 99.1 81.3 0.82 2.4 1.5 9.6 28.4 0.8 92.0 0 0 1 0 1 1 0 110 80 6 7 15.0 16.0 7.0
0 39 55.0 1.6 21.5 15 72 20 12.0 2 5 14.0 0 0 3.9 3.900 7.3 2.5 2.9 96.5 86.4 0.90 5.0 3.0 38.1 30.5 0.2 95.0 1 0 0 0 1 0 1 110 70 1 2 15.0 11.0 7.3
1 30 72.0 1.6 28.1 11 72 22 12.2 4 2 1.5 1 0 297.2 297.210 11.6 2.1 5.6 114.3 96.5 0.84 2.1 1.0 14.1 32.4 0.4 108.0 1 1 1 1 1 1 0 110 80 12 6 17.0 18.0 12.0
1 28 56.0 1.6 21.9 15 74 20 12.0 2 5 7.0 1 0 277.3 277.280 4.9 2.0 2.5 99.1 81.3 0.82 1.6 1.1 19.0 34.1 0.5 100.0 1 0 1 0 1 1 0 120 70 7 12 15.0 18.0 11.0
0 38 50.0 1.6 19.5 15 72 22 12.0 2 5 13.0 1 0 2.0 783.360 3.7 2.4 1.5 96.5 81.3 0.84 0.5 3.0 53.4 42.5 0.3 100.0 0 0 0 0 1 1 1 110 80 7 9 11.0 15.0 7.8
1 34 55.0 1.6 21.5 11 78 20 11.0 2 6 10.0 0 0 2.0 1.990 6.5 5.6 1.2 91.4 81.3 0.89 3.0 5.4 24.7 35.8 0.3 100.0 0 1 1 0 1 1 0 120 80 5 5 12.0 10.0 11.0
0 28 65.0 1.7 22.5 11 72 18 9.0 2 6 5.0 1 0 21084.2 21084.210 3.8 0.6 6.1 96.5 86.4 0.90 1.8 3.0 28.6 52.4 0.2 120.0 0 0 0 0 0 1 0 110 80 8 12 14.0 18.0 8.5
0 30 66.0 1.5 29.3 11 72 18 12.0 2 5 7.0 1 0 409.9 409.850 3.4 1.3 2.6 101.6 91.4 0.90 1.7 2.2 10.4 36.5 0.4 110.0 1 0 0 1 1 1 0 110 80 8 4 16.0 17.0 10.0
0 28 53.0 1.6 20.7 11 74 18 10.8 2 6 4.0 1 0 17244.0 410.130 4.9 0.6 8.4 91.4 76.2 0.83 1.1 2.1 21.2 22.2 0.3 80.0 0 0 0 1 1 1 0 100 70 4 6 14.0 18.0 7.7
0 47 60.0 1.6 23.4 13 72 18 13.0 2 5 24.0 1 0 479.6 479.600 4.6 1.4 3.3 91.4 81.3 0.89 5.9 3.3 13.8 35.4 0.4 100.0 0 0 0 1 1 1 0 110 80 6 6 15.0 18.0 8.4
0 32 60.0 1.5 26.7 15 72 18 13.8 2 5 5.0 1 0 545.8 545.780 6.0 3.2 1.9 96.5 86.4 0.90 3.3 1.0 22.1 25.1 0.4 85.0 1 0 0 1 1 1 0 120 80 1 2 16.0 17.0 6.4
1 33 64.0 1.6 25.0 13 72 18 10.2 2 5 13.0 1 0 784.0 1.990 3.2 1.4 2.2 91.4 76.2 0.83 0.8 2.6 18.1 33.5 0.2 82.0 0 1 1 1 1 1 0 110 80 7 20 15.0 17.0 12.0
0 38 50.0 1.5 22.2 17 72 18 12.0 4 3 12.0 0 0 2.0 1.990 4.3 2.6 1.6 86.4 76.2 0.88 2.4 11.0 32.2 29.4 0.3 80.0 0 0 0 1 1 1 0 110 80 5 11 8.0 12.0 10.5
0 28 61.0 1.5 27.1 11 72 24 10.8 2 6 6.5 0 0 2.0 1.990 7.0 2.3 3.1 99.1 88.9 0.90 0.2 3.6 26.9 32.4 0.3 85.0 1 0 0 0 1 1 0 110 80 7 11 15.0 18.0 8.0
1 37 56.0 1.6 21.9 17 78 22 12.0 4 2 14.0 1 0 320.5 320.490 2.1 0.8 2.6 94.0 86.4 0.92 4.6 5.7 63.3 35.7 0.2 110.0 1 0 1 1 1 1 0 110 80 14 19 18.0 19.0 12.0
0 40 64.0 1.6 25.0 13 74 28 10.8 2 5 17.0 0 0 2.0 1.990 6.2 5.0 1.2 99.1 88.9 0.90 7.3 1.0 35.7 27.6 0.3 100.0 1 0 0 0 1 1 1 110 80 4 7 10.0 16.0 9.0
0 35 57.0 1.6 22.3 15 80 22 10.6 2 6 17.0 1 0 12.0 12.000 4.1 1.3 3.1 91.4 81.3 0.89 4.0 6.3 26.0 23.4 0.3 85.0 0 0 0 1 1 1 1 110 70 4 4 10.0 15.0 11.0
0 45 54.0 1.5 24.0 13 72 16 10.6 2 6 23.0 0 3 3.0 78.380 5.3 3.0 1.7 96.5 88.9 0.92 3.7 2.5 22.3 37.9 0.5 100.0 0 0 0 1 1 0 1 120 80 2 4 10.0 15.0 13.0
0 31 55.0 1.6 21.5 11 74 18 10.2 2 5 11.0 1 0 204.7 204.690 3.8 4.8 0.8 94.0 86.4 0.92 0.4 2.2 20.3 32.6 0.3 85.0 0 0 0 1 1 0 1 110 70 4 3 18.0 13.0 9.0
1 30 65.0 1.6 25.4 11 70 18 11.0 4 2 7.0 1 0 337.0 12.000 6.9 14.2 0.5 99.1 86.4 0.87 2.3 15.7 8.8 18.7 0.3 100.0 1 1 1 0 1 1 0 110 80 7 10 13.0 17.0 8.0
0 29 53.0 1.6 20.7 15 72 18 10.2 2 6 5.0 1 0 900.6 900.600 5052.0 3.7 1372.8 96.5 88.9 0.92 0.8 3.5 30.6 28.6 0.3 108.0 0 0 0 1 1 1 0 110 80 6 4 16.0 17.0 6.0
0 33 60.0 1.6 23.4 11 72 18 10.3 2 6 13.0 0 0 2.0 1.990 5.9 1.3 4.6 96.5 88.9 0.92 0.7 1.1 1.4 20.3 0.2 100.0 0 0 0 1 1 0 0 120 80 1 2 12.0 18.0 9.0
0 29 59.0 1.6 23.0 17 70 18 10.7 4 3 12.0 1 0 12.0 12.000 6.5 1.5 4.4 96.5 86.4 0.90 1.6 1.3 34.1 32.4 0.2 92.0 0 0 0 0 0 1 0 110 80 3 3 13.0 10.0 6.0
0 27 59.0 1.6 23.0 15 72 18 10.0 2 6 6.0 1 0 159.7 159.710 1.9 0.7 2.8 91.4 81.3 0.89 2.1 3.0 23.1 34.5 0.2 92.0 0 0 0 1 1 0 0 110 70 3 3 14.0 12.0 10.5
0 31 45.0 1.5 20.0 13 80 20 10.8 2 5 8.0 1 0 15.0 15.000 3.9 2.5 1.6 88.9 76.2 0.86 2.2 3.0 23.2 47.5 0.2 100.0 0 0 1 1 0 0 0 110 80 4 1 11.0 10.0 12.0
0 30 62.0 1.5 27.6 15 74 18 11.5 2 5 6.0 1 0 296.3 296.310 0.2 0.9 0.2 96.5 88.9 0.92 1.8 2.2 12.1 22.9 0.4 77.0 1 0 0 1 1 1 0 120 80 6 7 15.0 11.0 11.0
0 30 46.0 1.6 18.0 15 78 22 10.8 2 5 5.0 1 0 8013.0 4176.000 2.4 0.2 11.1 86.4 78.7 0.91 0.8 3.3 15.0 13.4 0.9 100.0 0 0 0 1 1 0 0 110 80 2 2 14.0 15.0 9.0
1 21 82.0 1.6 32.0 16 78 24 10.6 4 3 3.0 1 0 2.0 183.060 3.4 1.4 2.5 111.8 96.5 0.86 5.0 14.7 31.7 33.7 0.2 85.0 1 1 1 1 1 1 0 110 80 8 10 15.0 12.0 10.0
1 36 89.0 1.6 34.8 15 78 24 14.0 4 3 5.0 0 0 2.0 1.990 7.1 2.2 3.2 111.8 101.6 0.91 4.2 2.8 10.2 35.1 0.2 130.0 1 1 1 1 1 1 0 110 80 16 12 11.0 9.0 9.0
1 31 73.0 1.6 28.5 15 78 24 10.7 4 2 7.0 0 0 2.0 1.990 3.6 5.1 0.7 106.7 96.5 0.90 1.7 1.1 14.1 29.1 0.8 92.0 1 1 1 0 0 1 0 110 80 12 10 10.0 10.0 9.3
0 29 53.0 1.5 23.6 17 78 22 10.6 2 5 2.0 1 0 2.0 20.000 5.7 4.3 1.3 96.5 86.4 0.90 4.0 9.1 20.8 19.7 0.3 123.0 0 0 1 0 0 1 1 110 80 1 1 10.0 12.0 8.2
1 26 60.0 1.5 26.7 13 78 22 12.5 4 2 5.0 1 0 161.8 161.770 6.8 4.6 1.5 101.6 91.4 0.90 2.4 5.9 34.2 38.6 0.3 76.0 1 1 0 0 1 1 0 100 80 10 15 14.0 11.0 10.0
1 26 65.0 1.6 25.4 15 75 24 10.5 2 5 7.0 1 0 62.0 61.980 7.1 7.9 0.9 101.6 88.9 0.88 2.3 8.9 32.4 23.4 0.3 107.0 1 1 0 0 1 1 0 110 80 10 7 10.0 11.0 8.7
1 32 40.0 1.6 15.6 15 78 22 11.2 2 5 12.0 1 0 403.9 403.850 6.7 3.2 2.1 91.4 81.3 0.89 1.8 3.6 13.3 57.7 0.2 107.0 0 0 0 1 1 1 0 110 80 4 15 15.0 12.0 11.0
0 25 85.0 1.6 33.2 11 78 22 10.2 2 5 3.0 1 2 136.7 1.990 7.1 5.2 1.4 106.7 96.5 0.90 2.1 2.3 19.2 31.4 0.2 100.0 1 0 0 1 0 1 0 110 80 10 5 15.0 17.0 8.0
0 36 62.0 1.6 24.2 13 78 22 11.0 2 6 14.0 1 0 34.6 34.650 4.5 1.2 3.6 101.6 91.4 0.90 0.7 3.6 8.4 30.1 0.2 107.0 0 0 0 1 1 1 0 110 80 3 5 10.0 11.0 9.2
0 30 50.0 1.5 22.2 15 78 22 10.5 4 3 2.0 1 0 48.9 48.860 4.6 0.3 15.4 96.5 88.9 0.92 1.1 2.8 5.7 36.9 0.2 92.0 0 0 0 0 1 1 0 110 80 1 1 10.0 10.0 13.0
1 28 83.0 1.6 32.4 11 74 20 11.1 4 2 7.0 0 1 2.0 1.990 5.5 3.0 1.8 106.7 94.0 0.88 20.9 20.4 14.6 10.5 0.5 110.0 1 1 0 1 1 1 0 110 80 10 12 15.0 17.0 10.0
0 31 56.0 1.6 21.9 11 72 18 10.2 2 6 10.0 1 0 371.7 371.740 1.8 0.3 5.1 96.5 88.9 0.92 0.5 1.5 20.9 34.0 0.2 100.0 0 0 0 1 1 1 0 110 70 6 4 10.0 11.0 11.0
0 32 57.0 1.6 22.3 13 72 20 10.8 2 5 4.0 0 0 2.0 1.990 4.0 1.4 2.8 96.5 86.4 0.90 4.3 4.6 15.4 26.1 0.4 80.0 0 0 1 0 1 1 1 110 70 4 8 11.0 15.0 10.8
0 30 71.0 1.6 27.7 13 72 18 11.0 4 2 5.0 0 0 2.0 1.990 7.6 4.7 1.6 106.7 91.4 0.86 1.4 5.0 20.4 13.9 0.6 92.0 1 0 0 1 1 1 0 110 80 1 3 12.0 15.0 14.0
0 22 56.0 1.5 24.9 13 72 18 10.8 2 6 1.0 1 0 213.5 1.990 4.7 3.7 1.3 99.1 86.4 0.87 3.6 3.9 25.5 35.5 1.2 100.0 0 0 0 1 1 1 0 110 80 10 6 15.0 12.0 9.0
1 36 54.0 1.5 24.0 13 78 24 10.2 4 2 15.0 1 0 970.8 970.750 5.3 9.9 0.5 96.5 83.8 0.87 1.4 9.8 8.1 42.1 0.5 85.0 0 1 0 1 1 1 0 100 80 10 7 13.0 15.0 9.0
1 27 60.0 1.6 23.4 15 72 18 10.7 2 6 2.5 1 0 109.1 109.060 3.2 3.0 1.1 101.6 88.9 0.88 3.0 3.6 18.9 36.4 0.2 100.0 0 1 1 0 0 1 0 120 80 4 5 15.0 18.0 8.0
0 30 55.0 1.6 21.5 13 74 22 10.2 2 6 7.0 1 1 1082.8 1082.820 5.7 2.8 2.0 94.0 86.4 0.92 1.8 3.5 24.5 31.2 0.2 92.0 0 0 0 1 1 1 0 110 80 6 4 13.0 15.0 5.6
0 33 50.0 1.5 22.2 15 80 20 9.8 4 2 12.0 1 0 739.1 739.130 6.7 4.0 1.7 96.5 88.9 0.92 2.4 10.7 24.5 45.4 0.3 85.0 0 0 0 1 1 1 0 110 80 7 4 15.0 18.0 10.6
0 28 61.0 1.6 23.8 15 72 18 10.5 2 6 6.0 1 0 288.7 288.720 4.2 0.3 15.4 101.6 88.9 0.88 6.3 4.8 54.9 17.4 0.2 100.0 0 1 0 0 1 0 1 110 80 5 7 15.0 18.0 7.0
1 32 80.4 1.7 27.8 18 72 18 11.5 4 3 10.0 0 0 4.7 1.990 4.5 0.2 21.3 106.7 96.5 0.90 2.1 4.5 48.6 19.3 0.3 105.0 1 1 1 0 1 1 0 120 80 9 12 15.0 15.0 11.0
0 32 47.0 1.6 18.4 15 74 20 10.2 2 6 7.0 1 0 18.5 18.490 4.5 3.3 1.4 94.0 83.8 0.89 1.4 2.6 19.8 18.8 1.0 115.0 0 1 0 0 1 0 0 110 80 1 1 14.0 16.0 6.4
0 34 53.0 1.4 27.0 12 78 20 10.8 2 6 10.0 1 0 169.3 169.330 3.3 0.6 5.2 96.5 88.9 0.92 1.5 3.9 31.8 23.9 0.4 115.0 1 0 1 0 0 1 0 120 70 2 2 10.0 15.0 12.0
0 36 61.0 1.5 27.1 13 70 18 10.5 2 6 12.0 1 0 418.9 418.900 2.9 1.7 1.7 96.5 88.9 0.92 1.5 3.1 26.1 20.2 0.3 95.0 1 0 0 1 1 0 1 120 80 2 3 13.0 11.0 6.8
0 34 61.0 1.5 27.1 15 72 18 10.5 2 5 7.0 0 0 2.0 1.990 6.4 3.5 1.9 99.1 86.4 0.87 2.6 10.9 14.5 39.1 0.2 100.0 1 0 0 1 0 0 0 120 70 3 3 13.0 14.0 13.5
0 35 62.0 1.5 27.6 13 78 20 10.8 2 6 17.0 0 1 2.0 1.990 2.7 1.4 1.9 101.6 91.4 0.90 1.9 5.2 28.3 30.9 0.3 92.0 1 0 0 1 1 0 1 120 70 2 3 10.0 10.0 7.0
0 42 50.0 1.6 19.5 15 74 16 10.0 4 3 5.0 1 0 14.8 53.820 4.9 0.7 7.3 94.0 81.3 0.86 1.0 1.6 23.6 21.3 0.2 82.0 0 0 0 1 0 0 0 120 80 1 1 14.0 13.0 9.2
0 31 48.0 1.5 21.3 17 72 18 11.0 2 6 8.0 0 0 2.0 1.990 6.0 1.0 6.0 88.9 76.2 0.86 3.1 1.0 20.8 43.4 0.6 100.0 0 0 0 1 1 1 0 110 80 1 3 13.0 13.0 7.0
0 35 48.0 1.6 18.7 15 72 20 10.8 2 5 10.0 1 1 650.7 1.990 7.8 3.6 2.1 91.4 76.2 0.83 1.4 6.4 29.6 24.6 0.6 110.0 0 0 1 0 0 1 0 120 80 3 3 16.0 15.0 9.0
0 38 77.9 1.6 30.4 15 72 24 11.1 2 5 2.0 0 0 2.0 1.990 4.3 1.4 3.2 106.7 94.0 0.88 1.1 2.8 19.2 20.5 0.3 100.0 1 1 0 0 1 1 0 140 100 2 2 15.0 14.0 8.5
1 23 67.0 1.5 29.8 11 72 20 10.0 4 2 2.0 0 0 2.0 1.990 3.2 2.3 1.4 96.5 81.3 0.84 1.3 6.4 23.0 15.9 0.3 92.0 1 0 1 1 1 0 0 120 80 8 11 13.0 15.0 9.8
0 20 64.0 1.7 22.1 15 72 18 11.5 2 6 8.0 1 0 10.4 10.450 2.1 0.1 13.9 96.5 88.9 0.92 5.0 2.2 50.5 28.7 0.2 86.0 0 0 0 1 1 1 0 120 80 5 7 15.0 14.0 11.0
1 22 58.0 1.6 22.7 13 74 22 10.0 4 3 5.0 1 0 764.8 764.830 4.1 3.9 1.0 96.5 86.4 0.90 5.0 15.0 37.5 22.0 0.6 92.0 0 1 0 1 1 1 0 120 80 8 11 15.0 14.0 10.0
0 35 60.0 1.5 26.7 15 74 18 10.0 2 5 3.0 1 0 116.3 116.310 6.5 3.4 1.9 99.1 91.4 0.92 0.8 2.5 40.7 20.0 0.3 92.0 1 0 0 1 0 1 0 110 80 3 4 12.0 15.0 8.0
1 28 58.0 1.6 22.7 13 72 18 11.5 2 6 1.0 1 0 785.4 300.130 6.2 4.4 1.4 94.0 86.4 0.92 0.6 4.2 27.2 26.0 0.3 100.0 0 1 1 0 1 1 1 110 80 15 12 18.0 20.0 12.0
0 31 68.0 1.6 26.6 15 72 18 12.8 2 6 6.0 1 0 96.2 5.390 1.6 0.2 6.6 99.1 88.9 0.90 6.9 5.1 15.8 25.6 0.5 87.0 1 0 0 1 1 1 0 110 80 4 9 12.0 17.0 9.0
1 29 74.0 1.6 28.9 11 72 20 10.2 4 3 3.0 1 1 346.6 346.590 5.8 6.4 0.9 101.6 94.0 0.93 0.9 6.0 45.5 36.2 0.3 92.0 1 1 1 0 0 1 0 120 80 12 9 17.0 19.0 8.0
0 28 42.0 1.6 16.4 13 70 18 12.0 2 5 7.0 1 0 348.0 1.990 2.6 0.6 4.1 88.9 76.2 0.86 5.7 5.8 33.0 39.3 0.4 84.0 0 0 0 1 1 0 0 120 70 8 7 15.0 15.0 11.0
0 32 65.0 1.6 25.4 11 74 20 10.2 2 6 11.0 1 0 6.2 6.190 2.2 2.0 1.1 96.5 86.4 0.90 4.8 0.2 99.9 17.3 0.2 115.0 1 0 0 0 1 1 0 110 70 1 2 13.0 14.0 7.5
1 27 60.0 1.6 23.4 14 72 20 11.2 2 4 8.0 1 0 187.8 187.790 5.4 3.1 1.8 91.4 81.3 0.89 6.8 9.0 40.4 23.1 0.5 100.0 0 1 0 0 1 0 1 120 80 9 10 14.0 12.0 10.3
0 35 60.0 1.5 26.7 11 72 18 10.0 2 6 10.0 1 0 459.6 459.630 3.9 1.0 3.9 96.5 88.9 0.92 2.5 1.2 8.6 20.7 0.3 115.0 0 0 1 0 1 1 0 120 80 1 2 18.0 11.0 9.0
0 28 61.0 1.5 27.1 15 74 22 10.5 4 3 3.0 0 0 2.0 3.010 3.3 0.6 5.6 99.1 88.9 0.90 2.8 9.1 7.8 10.1 0.3 92.0 1 0 0 0 1 1 0 120 80 8 7 19.0 15.0 5.5
0 23 50.0 1.6 19.5 15 72 18 10.8 4 6 3.0 0 0 2.0 2.000 9.6 6.0 1.6 94.0 86.4 0.92 3.6 2.9 30.9 21.1 0.8 92.0 0 0 0 1 1 0 0 110 70 3 5 18.0 18.0 11.2
0 31 55.5 1.5 24.7 15 74 18 11.1 2 6 5.0 1 0 278.3 278.520 2.5 3.4 0.7 96.5 86.4 0.90 1.6 3.1 24.3 24.1 0.2 92.0 0 0 1 0 1 1 0 110 80 6 7 18.0 22.0 9.0
0 37 65.0 1.6 25.4 15 72 20 10.3 2 5 6.0 0 0 2.0 1.990 6.5 1.4 4.5 96.5 86.4 0.90 1.4 5.6 19.3 24.1 0.2 82.0 1 0 0 0 1 1 0 120 80 6 6 12.0 15.0 10.0
0 35 60.0 1.5 26.7 15 82 20 10.0 4 3 10.0 0 0 2.0 1.990 2.8 2.8 1.0 94.0 88.9 0.95 1.6 7.9 15.4 21.7 0.2 110.0 1 0 0 1 0 1 0 120 80 5 5 13.0 15.0 6.5
0 29 46.0 1.5 20.4 11 72 20 10.8 2 6 6.0 1 1 10.8 10.840 5.1 1.1 4.8 86.4 83.8 0.97 2.7 18.9 52.3 40.3 0.3 110.0 0 0 0 1 1 1 0 120 80 8 5 16.0 12.0 7.0
0 32 60.5 1.6 23.6 13 72 18 11.0 2 6 9.0 0 2 2.0 1.990 4.6 1.5 3.0 96.5 86.4 0.90 1.4 11.4 25.5 20.1 0.2 100.0 1 0 0 0 1 0 1 120 80 2 3 18.0 20.0 8.0
0 37 50.0 1.5 22.2 15 72 18 10.2 2 7 5.0 1 0 184.4 184.420 4.4 1.4 3.1 94.0 83.8 0.89 2.7 3.8 18.9 19.5 0.4 100.0 0 0 0 1 0 1 0 110 80 5 3 15.0 17.0 7.5
0 41 76.9 1.7 26.6 15 72 18 11.0 4 3 14.0 0 0 4.2 4.200 6.5 6.7 1.0 106.7 96.5 0.90 0.6 4.5 19.8 18.6 0.5 100.0 1 0 1 1 0 1 0 130 80 4 2 14.0 11.0 7.0
0 40 63.0 1.7 21.8 15 72 18 11.0 2 6 17.0 0 0 2.0 1.990 1.9 0.5 4.0 96.5 86.4 0.90 5.0 2.0 42.7 20.8 0.8 138.0 0 0 1 0 0 1 0 110 70 3 1 11.0 12.0 8.3
1 24 60.0 1.5 26.7 11 74 16 11.0 2 5 8.0 1 0 305.8 1.990 6.0 2.7 2.2 96.5 81.3 0.84 3.3 4.5 28.0 19.3 0.2 125.0 1 0 1 1 1 1 0 110 70 16 14 12.0 15.0 7.5
0 31 55.5 1.5 24.7 15 74 18 11.1 2 5 5.0 1 0 278.3 278.320 2.5 3.4 0.7 94.0 86.4 0.92 1.6 3.1 24.3 24.1 0.2 92.0 0 0 0 0 1 1 1 110 80 6 7 18.0 12.0 9.0
0 23 50.0 1.6 19.5 15 72 18 10.8 4 2 3.0 0 0 2.0 2.000 9.6 6.0 1.6 91.4 81.3 0.89 3.6 2.9 30.9 21.1 0.8 92.0 0 0 0 1 0 0 1 110 70 3 5 15.0 12.0 11.2
1 28 61.0 1.5 27.1 15 74 20 10.5 4 2 3.0 0 0 2.0 3.010 3.3 0.6 5.6 96.5 88.9 0.92 2.8 9.1 7.8 10.1 0.3 92.0 1 0 0 1 1 1 0 120 80 10 9 19.0 15.0 10.0
0 35 60.0 1.5 26.7 11 72 18 10.0 2 6 10.0 1 0 459.6 459.630 3.9 1.0 3.9 99.1 91.4 0.92 2.5 1.2 8.6 20.7 0.3 115.0 1 0 0 1 0 0 0 120 80 1 2 15.0 11.0 9.0
1 27 45.0 1.6 17.6 14 72 20 11.2 2 5 8.0 1 0 187.8 187.790 5.4 3.1 1.8 91.4 81.3 0.89 6.8 9.0 40.4 23.1 0.5 100.0 0 0 1 0 0 1 1 120 80 10 11 15.0 13.0 10.3
0 32 65.0 1.6 25.4 11 74 20 10.2 2 5 11.0 1 0 6.2 6.190 2.2 2.0 1.1 96.5 88.9 0.92 4.8 0.2 99.9 17.3 0.2 115.0 1 0 0 0 0 1 0 110 70 1 2 13.0 15.0 7.5
0 28 42.0 1.6 16.4 13 70 18 12.0 2 6 7.0 1 0 348.0 1.990 2.6 0.6 4.1 88.9 76.2 0.86 5.7 5.8 33.0 39.3 0.4 84.0 0 0 1 0 1 1 0 120 70 8 7 16.0 15.0 11.0
1 29 74.0 1.6 28.9 11 72 20 10.8 4 3 3.0 1 0 346.6 346.590 5.8 6.4 0.9 106.7 96.5 0.90 0.9 6.0 45.5 36.2 0.3 92.0 1 0 1 1 1 1 0 120 80 10 9 15.0 14.0 8.0
0 31 68.0 1.6 26.6 15 72 18 12.8 2 6 6.0 1 0 96.2 5.390 1.6 0.2 6.6 101.6 94.0 0.93 6.9 5.1 15.8 25.6 0.5 87.0 1 0 0 1 1 0 0 110 80 4 9 12.0 15.0 7.0
0 35 60.0 1.5 26.7 15 74 18 10.0 2 6 3.0 1 0 116.3 116.310 6.5 3.4 1.9 96.5 86.4 0.90 0.8 2.5 40.7 20.0 0.3 92.0 1 0 0 0 1 0 1 110 80 3 4 12.0 11.0 8.0
1 36 60.0 1.5 26.7 11 72 18 11.2 2 4 5.0 1 1 232.6 232.640 3.7 1.6 2.3 96.5 88.9 0.92 1.6 1.6 18.4 24.6 0.5 92.0 1 1 0 0 1 1 0 110 80 6 9 19.0 17.0 5.8
0 30 67.0 1.7 23.2 13 80 20 10.0 2 6 5.0 1 0 167.4 167.410 5.7 2.4 2.4 99.1 88.9 0.90 2.3 1.9 25.1 12.4 0.6 100.0 0 0 0 1 0 1 0 120 70 3 3 18.0 19.0 9.6
0 27 58.0 1.6 22.7 13 74 18 10.5 2 5 8.0 0 0 2.0 1.990 2.1 1.1 1.9 94.0 86.4 0.92 3.4 1.1 10.8 25.4 0.2 100.0 0 0 0 1 1 0 0 120 70 4 4 14.0 16.0 7.0
0 28 65.6 1.6 25.6 11 72 20 10.5 2 6 5.0 0 0 2.0 2.000 4.4 1.0 4.3 99.1 91.4 0.92 3.7 5.7 23.6 15.5 0.6 130.0 1 0 0 1 0 1 0 120 80 5 5 14.0 16.0 8.0
1 28 65.0 1.6 25.4 11 74 20 10.5 4 3 4.5 1 0 230.5 230.530 5.2 6.0 0.9 91.4 81.3 0.89 1.6 28.6 12.6 21.2 0.2 125.0 1 0 1 1 1 1 0 110 70 11 12 19.0 17.0 11.0
1 27 64.0 1.6 25.0 13 78 22 10.8 2 5 3.0 0 0 2.0 2.000 3.1 0.6 5.6 91.4 81.3 0.89 2.3 5.5 28.9 25.1 0.2 80.0 1 0 1 1 0 1 0 120 80 4 11 18.0 20.0 7.0
0 26 38.0 1.5 16.9 15 72 18 10.5 2 6 3.0 1 0 10.4 10.400 2.7 2.5 1.1 71.1 63.5 0.89 1.5 3.8 42.0 47.8 0.5 130.0 0 0 1 0 0 0 0 120 70 4 5 18.0 20.0 9.5
0 33 50.0 1.6 19.5 15 74 18 10.2 2 5 2.0 1 0 465.0 465.010 3.4 2.8 1.2 88.9 78.7 0.89 2.8 4.2 19.1 43.3 0.3 82.0 0 0 0 1 1 1 0 120 80 2 3 14.0 24.0 6.8
0 33 45.0 1.4 23.0 11 72 20 11.5 2 6 9.0 0 0 2.0 1.990 2.4 0.9 2.7 88.9 78.7 0.89 1.7 0.5 38.5 23.7 0.3 85.0 0 0 0 1 0 1 0 120 80 1 3 17.0 20.0 9.0
0 36 59.0 1.6 23.0 15 74 20 10.0 2 5 17.0 0 0 2.0 2.000 5.2 1.8 2.9 96.5 86.4 0.90 1.9 0.9 22.8 25.9 0.4 100.0 0 0 0 0 1 1 0 120 80 2 3 17.0 18.0 11.0
0 45 57.0 1.6 22.3 15 72 22 10.3 2 6 23.0 0 0 2.0 1.990 4.1 3.6 1.1 96.5 88.9 0.92 2.0 4.8 20.2 17.4 0.4 130.0 0 0 1 0 1 1 0 110 70 3 6 14.0 12.0 9.0
0 40 60.0 1.6 23.4 13 74 20 11.2 2 6 13.0 1 0 41.8 41.750 5.1 3.0 1.7 96.5 88.9 0.92 2.8 1.3 11.8 16.1 0.2 146.0 0 0 0 1 1 0 0 110 70 3 0 10.0 11.0 4.0
0 34 54.7 1.6 21.4 13 72 20 10.4 2 5 9.0 1 0 363.1 8.540 6.7 8.7 0.8 94.0 83.8 0.89 3.6 6.4 12.3 25.8 4.2 100.0 0 0 0 1 1 1 1 120 80 1 1 13.0 15.0 8.0
0 40 53.0 1.5 23.6 15 78 20 11.4 2 5 14.0 1 0 382.4 382.360 4.8 3.8 1.2 86.4 76.2 0.88 2.0 8.1 8.1 44.1 0.2 115.0 0 0 0 1 0 1 0 120 70 3 4 2.0 13.0 11.0
1 23 55.0 1.5 24.4 13 78 18 10.8 2 5 3.0 0 0 2.0 1.990 40.1 2.0 20.1 91.4 81.3 0.89 1.4 10.8 43.9 29.4 0.4 108.0 0 1 1 1 1 0 1 110 70 10 18 14.0 14.0 10.3
0 26 52.0 1.5 23.1 15 74 20 10.0 2 5 9.0 0 0 2.0 1.990 3.5 2.1 1.6 88.9 78.7 0.89 0.7 5.2 15.6 30.1 0.3 115.0 0 0 0 1 1 1 0 110 70 1 1 13.0 14.0 13.0
1 30 64.0 1.6 25.0 11 74 22 10.2 2 4 2.2 1 0 2229.8 10.980 4.8 7.2 0.7 96.5 88.9 0.92 4.8 7.2 32.1 33.8 0.2 92.0 1 0 1 0 1 1 0 110 70 12 14 14.0 14.0 9.5
1 31 60.0 1.6 23.4 15 80 20 10.8 4 2 7.0 1 0 252.7 252.720 1.8 0.9 2.0 99.1 88.9 0.90 1.9 15.0 41.9 23.6 0.2 92.0 0 1 1 0 1 1 0 120 80 8 12 12.0 16.0 6.5
0 26 45.0 1.5 20.0 13 74 20 10.2 2 5 8.0 1 0 67.2 67.200 7.1 3.8 1.9 81.3 71.1 0.87 1.0 3.4 10.8 28.1 0.2 100.0 0 0 0 1 0 1 0 120 80 6 2 14.0 13.0 8.0
0 32 71.8 1.6 28.0 11 72 18 10.8 2 6 4.0 0 0 2.0 1.990 3.7 0.1 28.3 104.1 96.5 0.93 2.6 2.3 20.9 27.1 0.2 92.0 1 0 1 0 1 0 1 120 80 3 3 14.0 13.0 9.0
1 27 65.0 1.6 25.4 15 74 20 11.2 2 4 2.0 0 0 2.0 1.990 3.9 1.0 3.8 94.0 86.4 0.92 2.4 2.6 9.3 15.6 0.2 110.0 1 0 1 0 1 1 0 110 80 4 9 10.0 15.0 6.7
0 36 58.0 1.5 25.8 15 72 18 11.0 2 5 12.0 0 0 2.0 1.990 4.8 2.5 1.9 91.4 81.3 0.89 0.9 4.8 26.4 25.4 0.3 92.0 0 0 0 1 1 1 0 110 80 3 3 13.0 15.0 7.8
1 25 45.6 1.5 20.3 11 74 20 11.0 2 5 1.0 1 0 6.9 6.921 5.1 3.6 1.4 76.2 66.0 0.87 1.9 11.9 20.5 29.3 0.2 115.0 0 1 0 1 1 1 0 120 80 14 10 15.0 15.0 10.0
0 40 74.1 1.6 28.9 15 78 20 11.5 2 6 18.0 0 0 2.0 1.990 1.0 0.6 1.7 106.7 96.5 0.90 4.4 6.9 26.4 17.1 0.3 92.0 1 0 1 0 1 1 1 120 80 2 4 10.0 16.0 7.2
1 38 68.0 1.6 26.6 15 74 20 10.4 4 2 1.0 1 0 482.2 482.210 7.1 4.3 1.7 96.5 88.9 0.92 4.0 0.1 6.2 25.3 0.4 107.0 1 0 1 1 1 1 0 120 80 13 12 15.0 15.0 8.2
0 42 55.0 1.5 24.4 13 74 20 10.7 2 5 5.0 1 0 40.2 1.990 3.0 0.2 14.8 91.4 81.3 0.89 3.6 12.8 37.9 23.1 0.3 107.0 0 0 0 1 1 1 0 120 80 4 5 14.0 10.0 11.0
0 29 51.0 1.6 19.9 15 72 20 11.0 2 5 7.0 1 0 50.1 0.990 2.0 1.8 1.1 81.3 71.1 0.87 1.3 2.7 71.8 23.5 0.2 100.0 0 0 0 0 0 1 1 110 80 6 3 12.0 11.0 8.5
1 37 59.0 1.5 26.2 11 74 20 10.8 4 3 4.0 0 0 2.0 1.990 7.6 4.6 1.7 91.4 81.3 0.89 3.5 6.0 13.6 26.4 0.3 92.0 1 0 1 0 1 1 0 120 80 8 12 17.0 0.2 11.2
0 33 64.0 1.6 25.0 13 74 20 11.0 2 5 12.0 1 1 103.5 103.500 3.4 0.6 6.0 91.4 81.3 0.89 3.0 0.8 26.0 23.8 0.2 106.0 1 0 0 1 0 0 1 110 80 7 3 19.0 17.0 9.0
1 32 63.0 1.6 24.6 11 72 18 10.6 4 2 10.0 1 1 102.9 102.870 3.3 2.3 1.4 91.4 78.7 0.86 0.9 2.9 68.0 18.1 0.4 115.0 1 0 1 1 1 0 0 110 70 3 11 19.0 17.0 8.9
1 28 48.0 1.5 21.3 11 74 20 10.2 2 6 4.0 1 0 90.7 90.670 4.0 1.6 2.5 76.2 66.0 0.87 1.1 4.6 64.5 34.4 0.2 100.0 0 0 1 1 1 1 0 110 70 11 9 19.0 15.0 11.5
1 33 60.0 1.5 26.7 13 72 18 12.5 2 5 6.0 1 0 375.2 375.180 2.5 1.2 2.1 96.5 86.4 0.90 2.5 20.0 25.4 35.1 0.2 92.0 1 0 1 1 1 1 0 130 80 14 16 16.0 16.0 10.2
1 25 65.0 1.5 28.9 13 72 20 11.0 4 3 5.0 1 0 728.0 728.010 4.6 2.3 2.0 91.4 81.3 0.89 2.4 8.9 24.8 18.7 0.5 72.0 1 0 1 0 0 0 1 120 80 4 6 13.0 14.0 7.6
0 42 65.0 1.6 25.4 11 72 18 10.5 2 6 2.0 1 0 434.0 433.970 6.8 1.8 3.8 96.5 88.9 0.92 3.5 1.2 12.4 22.7 0.3 115.0 1 0 0 0 1 1 0 110 70 5 7 14.0 15.0 7.0
0 29 50.0 1.5 22.2 15 72 22 11.0 2 5 5.0 0 0 2.0 1.990 5.6 2.7 2.1 81.3 71.1 0.87 1.2 2.8 15.3 32.7 0.2 225.0 0 0 0 1 1 0 0 120 70 4 6 10.0 13.0 7.0
0 33 57.0 1.6 22.3 15 72 18 11.5 2 6 10.0 0 0 2.0 1.990 4.1 2.6 1.6 81.3 71.1 0.87 1.1 10.3 10.6 27.2 0.2 100.0 0 0 0 1 1 1 1 110 70 5 7 13.0 11.0 10.0
1 23 70.0 1.6 27.3 13 72 18 11.0 2 5 2.0 0 0 2.0 1.990 4.5 1.5 3.0 106.7 96.5 0.90 2.0 9.9 17.5 18.0 0.5 92.0 1 0 1 1 1 1 0 110 70 8 11 14.0 15.0 11.0
0 30 64.0 1.7 22.1 15 72 18 11.1 2 5 6.0 0 0 1399.0 1.990 3.8 1.6 2.4 101.6 91.4 0.90 1.5 2.5 27.7 31.8 0.2 94.0 0 0 0 1 1 0 1 110 80 4 3 11.0 16.0 9.0
0 29 48.0 1.5 21.3 15 70 18 10.2 2 6 6.0 1 0 213.1 213.140 13.9 0.5 27.8 81.3 68.6 0.84 0.8 3.6 31.0 74.5 0.8 92.0 0 0 0 1 0 1 0 110 80 4 4 14.0 13.0 10.0
1 32 71.0 1.6 27.7 13 74 20 10.5 4 3 13.0 0 0 4.2 4.200 6.6 4.3 1.5 106.7 96.5 0.90 2.3 8.0 18.9 36.6 0.2 85.0 1 0 1 1 1 1 0 120 80 9 12 18.0 19.0 8.0
1 33 76.0 1.6 29.7 11 72 20 11.0 4 2 6.0 1 1 86.4 238.140 3.5 3.1 1.1 106.7 99.1 0.93 3.9 1.0 30.5 32.9 0.4 100.0 1 0 1 0 1 1 0 110 80 11 9 16.0 19.0 10.0
1 34 73.0 1.6 28.5 13 72 20 11.2 2 5 3.0 1 0 70.5 1.990 4.5 1.4 3.3 101.6 91.4 0.90 2.3 10.2 15.3 18.2 0.2 130.0 1 0 0 1 1 1 0 110 80 7 13 15.0 19.0 10.3
0 26 56.0 1.6 21.9 11 70 20 11.1 2 6 3.0 1 0 2.0 181.230 5.7 2.4 2.4 86.4 76.2 0.88 1.5 11.0 21.9 16.9 0.2 110.0 0 0 0 1 0 1 1 110 70 9 6 19.0 14.0 8.0
0 31 64.0 1.7 22.1 15 74 18 11.0 4 3 6.0 0 0 3.4 1.990 1.1 1.5 0.7 91.4 81.3 0.89 3.0 0.6 10.4 12.0 0.9 115.0 0 0 0 0 1 0 1 110 80 3 5 11.0 12.0 3.0
0 40 56.0 1.6 21.9 15 72 18 12.8 2 6 22.0 1 2 407.2 491.850 5.7 3.3 1.7 86.4 76.2 0.88 3.9 0.5 29.5 24.3 0.5 107.0 0 0 0 1 0 0 1 110 70 6 2 9.0 11.0 7.0
1 39 55.0 1.5 24.4 15 78 20 10.7 4 7 13.0 1 0 12.4 19.440 5.4 2.0 2.8 96.5 91.4 0.95 1.0 0.4 21.9 18.6 0.5 94.0 0 0 0 0 1 1 0 120 80 12 11 19.0 19.0 8.1
1 27 52.0 1.5 23.1 11 82 20 11.1 2 5 5.0 0 0 2.0 1.990 5.0 2.2 2.2 91.4 81.3 0.89 1.9 16.9 9.1 23.6 0.2 93.0 0 0 0 0 0 1 1 120 80 1 1 16.0 17.0 8.6
1 26 72.0 1.5 32.0 15 72 20 12.0 4 2 3.0 0 0 144.6 1.990 3.6 8.3 0.4 106.7 101.6 0.95 2.9 26.4 29.7 22.4 0.4 93.0 1 0 1 0 1 1 0 110 80 8 7 20.0 20.0 6.0
0 29 63.0 1.6 24.6 11 72 18 13.2 2 5 11.0 0 0 30004.0 475.040 2.0 1.6 1.2 99.1 94.0 0.95 1.8 6.0 22.0 22.7 0.5 95.0 0 1 1 0 0 1 0 110 80 6 7 18.0 19.0 5.9
1 47 62.7 1.6 24.5 15 72 18 10.0 4 7 30.0 0 0 30007.0 1.990 1.9 0.2 7.5 99.1 91.4 0.92 2.5 6.2 31.5 12.1 0.2 92.0 0 0 0 0 1 1 1 110 80 14 11 20.0 19.0 6.0
0 41 71.5 1.6 27.9 15 74 20 11.0 4 7 16.0 1 1 1.9 1.990 4.7 3.6 1.3 106.7 99.1 0.93 1.1 9.1 22.6 31.6 0.2 85.0 1 1 1 0 0 1 0 120 80 9 7 19.0 18.0 8.0
1 33 63.0 1.6 24.6 15 72 18 10.1 4 2 7.0 1 0 294.1 1.990 5.0 12.0 0.4 99.1 94.0 0.95 3.1 0.8 33.2 40.2 0.3 100.0 0 0 0 0 1 1 0 120 80 6 8 19.0 17.0 8.2
1 34 69.0 1.7 23.9 15 80 20 11.5 2 5 10.0 0 0 53.5 13.120 4.1 1.9 2.1 101.6 96.5 0.95 3.1 0.9 25.6 61.3 0.3 107.0 1 1 0 1 1 1 0 110 80 20 16 18.0 14.0 7.0
1 30 44.0 1.6 17.2 12 72 18 11.4 2 4 4.0 1 0 595.8 1.920 8.2 0.5 17.9 71.1 61.0 0.86 3.5 1.0 26.4 30.5 0.2 84.0 0 0 0 0 0 0 1 100 70 5 18 11.0 21.0 9.7
1 31 50.0 1.6 19.5 11 74 20 10.8 2 5 11.0 0 0 482.9 1.990 3.1 0.5 6.1 91.4 76.2 0.83 1.3 0.9 19.5 29.2 0.2 92.0 0 0 1 0 1 1 1 110 80 10 19 17.0 18.0 8.5
1 35 57.0 1.5 25.3 15 72 18 10.7 2 5 7.0 0 0 950.4 1.990 4.4 2.0 2.2 96.5 81.3 0.84 5.0 0.9 12.7 16.0 0.3 92.0 0 0 0 0 0 0 1 110 70 11 15 19.0 21.0 7.8
1 35 61.0 1.6 23.8 13 72 18 10.5 2 5 13.0 0 1 355.5 1.990 3.7 2.7 1.4 96.5 83.8 0.87 3.0 9.7 20.4 15.0 0.3 93.0 0 0 1 0 1 1 1 120 80 12 13 19.0 18.0 8.2
1 32 52.0 1.5 23.1 13 70 18 9.4 2 5 12.0 0 0 272.8 1.990 4.3 2018.0 0.0 91.4 81.3 0.89 2.1 7.7 40.5 41.0 0.2 93.0 0 0 0 0 1 0 1 110 80 6 7 18.0 18.0 9.0
1 31 51.0 1.6 19.9 17 78 20 11.1 2 6 5.0 0 0 148.5 1.990 5.0 1.8 2.8 91.4 78.7 0.86 2.2 1.1 12.1 25.7 0.3 93.0 0 0 1 0 1 0 1 120 70 14 10 17.0 17.0 7.0
1 31 38.0 1.6 14.8 15 80 20 12.0 4 2 3.0 0 0 1.9 1.990 4.0 6.6 0.6 66.0 63.5 0.96 1.3 17.6 13.9 19.5 0.2 133.0 0 0 0 0 1 0 0 120 70 7 6 17.0 19.0 8.0
1 36 66.0 1.6 25.8 15 72 20 11.0 4 7 NA 0 0 515.3 1.990 1.6 0.2 9.7 99.1 94.0 0.95 1.9 6.6 25.0 20.8 0.2 93.0 0 0 0 0 0 0 0 120 80 14 5 19.0 19.0 8.0
0 36 55.0 1.5 24.4 13 74 18 10.0 2 5 8.0 0 0 8.0 1.990 3.2 0.1 32.2 96.5 94.0 0.97 2.9 5.0 22.8 53.2 0.3 92.0 0 0 0 1 0 1 0 110 80 4 1 22.0 14.0 10.5
0 27 63.0 1.6 24.6 12 72 18 11.2 2 4 2.5 0 1 152.7 1.990 7.9 3.4 2.4 99.1 94.0 0.95 2.3 7.0 13.1 42.1 0.2 80.0 0 0 0 0 0 1 0 110 70 5 8 18.0 19.0 7.3
0 33 55.0 1.6 21.5 15 72 22 12.0 2 5 6.0 1 0 480.2 1.900 3.8 1.4 2.8 96.5 91.4 0.95 2.2 9.0 32.4 19.8 0.2 92.0 0 0 0 0 0 1 0 110 80 0 4 0.0 20.0 8.9
1 33 89.0 1.6 34.8 12 78 22 10.8 4 7 6.0 1 0 382.0 15.000 3.5 0.7 4.8 116.8 101.6 0.87 0.7 0.7 15.2 16.7 0.3 100.0 1 0 1 1 1 1 0 120 80 13 12 19.0 18.0 7.9
0 41 45.0 1.6 17.6 14 78 22 11.0 2 5 18.0 1 0 152.9 268.370 4.4 1.3 3.5 96.5 94.0 0.97 2.2 10.0 22.6 25.5 0.3 100.0 0 1 1 1 1 1 0 110 80 2 1 19.0 18.0 7.2
1 34 54.0 1.5 24.0 16 72 20 11.0 4 7 3.5 0 0 598.1 1.990 2.2 2.3 1.0 91.4 88.9 0.97 6.7 0.8 60.4 46.2 0.2 90.0 0 0 0 0 0 0 0 110 80 21 19 16.0 18.0 7.6
0 33 50.0 1.5 22.2 15 70 18 10.8 2 4 8.0 0 0 194.4 1.990 2.6 0.2 12.9 91.4 86.4 0.95 1.8 6.0 20.8 58.3 0.4 90.0 0 0 0 0 0 0 0 110 80 6 9 20.0 18.0 8.0
0 35 61.8 1.5 27.5 17 70 20 12.0 2 4 11.0 0 0 2.0 1.990 6.8 3.2 2.1 99.1 96.5 0.97 2.3 6.0 10.6 62.5 0.5 100.0 0 0 0 0 0 1 0 120 80 11 9 21.0 16.0 8.0
1 32 64.0 1.6 25.0 12 72 18 11.5 4 2 9.0 1 0 561.7 1.990 3.6 3.5 1.0 99.1 86.4 0.87 0.6 0.8 25.4 15.3 0.4 102.0 1 0 0 0 0 0 0 110 70 13 12 19.0 19.0 7.0
0 40 56.0 1.5 24.9 12 72 18 10.5 2 5 7.0 1 1 10.0 1.990 6.6 3.4 1.9 96.5 86.4 0.90 5.0 16.0 32.5 21.6 0.2 92.0 0 0 1 0 0 1 1 110 80 9 10 22.0 17.0 8.2
1 25 55.0 1.5 24.4 15 74 20 12.0 2 5 4.0 0 0 10.0 1.990 4.7 0.7 6.6 96.5 91.4 0.95 1.8 0.7 24.8 60.3 0.3 102.0 0 0 0 1 0 0 1 110 70 12 12 24.0 18.0 8.9
1 34 52.0 1.5 23.1 13 80 20 12.0 4 7 10.0 0 2 2.0 8.000 5.5 6.4 0.9 91.4 81.3 0.89 2.9 0.9 19.2 12.3 0.3 100.0 0 1 1 1 0 1 1 110 80 8 10 17.0 20.0 7.0
1 31 50.0 1.5 22.2 13 80 20 11.0 2 5 7.0 0 0 381.9 1.990 6.2 1.0 6.2 91.4 86.4 0.95 1.8 1.0 32.1 18.5 0.2 90.0 0 0 0 1 0 1 0 110 70 5 14 15.0 20.0 7.5
1 26 68.0 1.6 26.6 11 72 18 12.0 2 5 6.0 1 1 10.0 9.350 5.0 2.0 2.5 106.7 96.5 0.90 1.3 0.9 14.8 32.8 0.2 92.0 1 0 0 0 1 1 0 110 70 12 14 19.0 18.0 8.9
0 39 50.0 1.5 22.2 15 78 22 12.1 2 5 16.0 0 0 2.0 1.990 3.8 2.0 1.9 86.4 76.2 0.88 4.3 4.1 14.1 23.7 0.2 100.0 0 0 0 0 0 0 0 120 80 5 7 19.0 20.0 5.0
0 48 55.0 1.5 24.4 15 70 18 9.4 4 2 25.0 0 0 4983.2 127.200 4.1 2.6 1.6 96.5 91.4 0.95 0.4 4.0 27.7 29.2 0.3 91.0 0 0 0 0 1 0 1 130 80 9 10 18.0 19.0 9.0
1 31 55.0 1.6 21.5 13 72 20 12.1 2 4 7.0 1 0 2.0 56.400 6.5 7.5 0.9 96.5 81.3 0.84 2.3 6.1 14.8 30.8 0.2 84.0 0 0 1 0 0 0 1 120 80 6 15 18.0 20.0 8.5
0 32 61.0 1.7 21.1 17 78 20 11.5 2 5 1.5 1 0 2.0 1.990 9.1 3.4 2.7 101.6 88.9 0.88 0.6 0.7 20.1 36.3 0.6 116.0 0 0 0 0 0 0 1 110 80 2 1 20.0 17.0 6.4
1 42 94.0 1.6 36.7 16 74 18 10.7 4 7 16.0 0 0 501.3 1.990 5.6 2.0 2.8 121.9 119.4 0.98 1.9 3.6 7.4 28.2 0.6 91.0 1 1 1 1 0 1 0 130 80 18 11 20.0 19.0 9.2
0 28 60.0 1.6 23.4 15 72 18 11.7 2 5 4.0 0 0 21.4 168.990 8.2 2.8 2.9 99.1 94.0 0.95 1.0 0.9 33.2 26.3 0.4 91.0 0 0 0 0 0 0 0 120 80 3 5 18.0 17.0 6.0
0 27 60.0 1.6 23.4 11 72 20 10.4 2 5 7.0 1 0 696.8 1.990 5.9 2.4 2.5 99.1 96.5 0.97 1.0 2.0 15.8 25.3 0.4 116.0 0 0 1 1 1 0 0 110 80 5 8 18.0 18.0 9.0
0 26 52.0 1.6 20.3 13 70 18 10.0 2 5 3.0 0 0 22.0 1.990 6.8 0.4 15.8 91.4 88.9 0.97 1.5 1.4 49.0 24.5 0.2 84.0 0 0 0 0 0 0 0 110 80 7 5 20.0 21.0 8.0
0 44 66.1 1.5 29.4 13 70 20 10.4 2 5 8.0 0 0 1.9 1.990 7.8 2.0 3.9 101.6 86.4 0.85 3.2 3.0 13.8 27.5 0.4 125.0 1 0 0 0 1 0 0 130 80 5 9 16.0 19.0 4.5
0 32 43.0 1.6 16.8 13 72 22 12.1 2 5 7.0 1 0 2.0 1.990 5.5 4.1 1.3 86.4 76.2 0.88 2.3 3.9 40.3 32.6 0.4 108.0 0 0 0 0 1 1 1 110 80 1 3 19.0 19.0 8.6
1 33 87.9 1.6 34.3 15 72 20 10.0 4 2 8.0 0 0 70.3 1.990 2.8 0.4 7.6 116.8 111.8 0.96 6.6 5.8 28.0 21.0 0.2 116.0 1 0 0 0 1 1 0 120 80 7 12 20.0 14.0 8.0
0 31 63.0 1.6 24.6 11 72 20 10.8 2 5 5.0 0 0 2.0 1.990 5.3 3.5 1.5 99.1 91.4 0.92 2.5 6.3 28.1 16.2 25.3 116.0 0 1 0 1 0 1 1 120 80 9 10 18.0 17.0 7.2
0 30 44.8 1.6 17.5 15 80 20 10.0 2 5 9.0 1 0 1238.4 237.500 1.0 0.1 10.0 86.4 76.2 0.88 3.3 0.7 44.4 33.3 0.2 91.0 0 0 0 0 0 1 1 110 80 2 4 15.0 18.0 8.5
1 30 49.1 1.6 19.2 11 74 18 11.7 4 2 4.0 1 0 710.4 1.990 7.6 6.7 1.1 91.4 81.3 0.89 3.6 10.5 22.7 21.7 0.4 116.0 0 1 1 0 0 1 1 110 80 16 16 19.0 19.0 10.5
0 36 62.0 1.5 27.6 15 74 20 12.1 4 2 9.0 0 3 2.0 1.990 3.4 5.1 0.7 101.6 94.0 0.93 1.8 10.3 24.6 27.6 0.3 84.0 1 1 0 0 0 1 0 120 70 4 4 18.0 19.0 6.8
0 36 76.9 1.7 26.6 11 72 18 10.8 2 5 7.0 0 0 37.2 1.990 5.7 2.0 2.8 106.7 104.1 0.98 7.0 2.4 16.6 22.0 0.2 116.0 1 0 0 0 0 1 0 110 80 5 9 20.0 19.0 6.8
1 28 63.0 1.4 32.1 11 72 18 11.8 2 4 11.0 0 0 2.0 1.990 7.1 2.3 3.0 101.6 99.1 0.98 0.3 2.7 44.4 37.6 0.4 84.0 1 0 0 0 0 0 0 120 80 7 15 16.0 16.0 10.0
0 35 61.0 1.5 27.1 12 80 20 10.2 4 7 16.0 0 0 2.0 1.990 4.6 1.2 3.8 101.6 96.5 0.95 1.2 1.1 54.9 27.2 0.5 92.0 1 1 1 1 1 1 0 120 70 3 2 19.0 16.0 6.0
0 23 66.0 1.6 25.8 16 72 18 11.5 4 2 3.0 1 0 658.3 1.990 8.1 1.4 5.8 96.5 94.0 0.97 4.1 4.9 27.2 36.1 0.2 100.0 0 0 0 0 1 1 0 110 80 6 5 18.0 20.0 7.2
0 25 58.0 1.6 22.7 14 72 18 10.8 2 5 3.0 1 0 2.0 1.990 5.7 4.2 1.4 86.4 81.3 0.94 2.8 10.6 24.0 19.3 0.5 92.0 0 1 0 1 0 0 0 120 80 1 2 19.0 18.0 10.3
1 30 50.0 1.5 22.2 15 72 20 10.8 2 4 6.0 1 0 492.0 1.990 7.7 0.3 29.7 86.4 83.8 0.97 8.3 4.3 13.1 25.7 0.2 85.0 0 0 0 0 0 0 1 120 80 9 19 19.0 18.0 6.0
0 30 48.0 1.5 21.3 15 80 20 10.5 2 5 6.0 1 0 341.5 1.990 2.4 0.3 7.0 71.1 68.6 0.96 1.5 5.1 13.4 38.2 0.3 92.0 1 0 1 0 1 1 0 110 70 5 8 18.0 18.0 7.0
1 27 64.0 1.6 25.0 13 72 20 11.5 4 2 5.0 0 0 275.6 1.990 5.7 1.3 4.4 101.6 91.4 0.90 1.5 16.8 27.5 23.4 0.6 140.0 1 1 1 1 1 1 0 120 70 10 11 18.0 16.0 6.4
0 34 45.0 1.5 20.0 17 70 18 10.7 2 5 8.0 0 0 336.5 1.990 3.1 3.5 0.9 71.1 66.0 0.93 2.2 7.9 14.1 27.1 0.4 103.0 0 0 0 0 0 0 0 110 80 5 4 18.0 16.0 5.4
0 35 54.0 1.4 27.6 11 72 20 10.8 2 5 5.0 1 0 5.7 1.990 4.6 1.3 3.5 91.4 86.4 0.95 4.1 3.1 27.2 31.6 0.4 112.0 0 0 0 0 1 0 0 110 70 9 7 20.0 16.0 6.6
0 23 52.0 1.5 23.1 15 72 20 11.0 2 5 4.0 0 0 2.0 1.990 7.7 4.1 1.9 81.3 76.2 0.94 5.0 5.1 12.6 25.9 0.5 92.0 0 0 0 0 0 0 0 120 80 1 1 7.0 17.0 6.0
0 28 65.0 1.6 25.4 14 74 20 10.8 2 5 3.0 0 0 296.6 1.990 6.5 10.5 0.6 81.3 78.7 0.97 1.8 11.2 34.2 33.0 0.3 100.0 0 0 0 0 0 0 0 120 80 8 7 18.0 20.0 8.8
0 40 59.0 1.5 26.2 13 72 18 11.5 2 5 10.0 1 1 2.0 1.990 3.9 1.1 3.6 111.8 106.7 0.95 2.9 1.7 7.1 34.2 0.5 120.0 1 1 0 1 0 0 0 100 70 3 5 17.0 18.0 14.2
0 38 70.0 1.6 27.3 16 72 18 12.1 2 4 8.0 0 0 2.0 1.990 3.5 0.2 17.5 116.8 106.7 0.91 3.1 5.2 24.7 32.2 0.4 92.0 1 1 0 0 1 0 0 110 70 2 7 19.0 19.0 9.0
0 38 52.0 1.5 23.1 15 80 20 11.0 2 5 15.0 0 0 469.6 1.990 6.8 5.8 1.2 91.4 88.9 0.97 1.2 2.2 37.6 25.3 0.2 100.0 0 0 0 0 0 0 0 120 80 3 1 21.0 19.0 9.3
0 31 52.9 1.5 23.5 16 70 20 11.0 2 5 9.0 0 0 147.9 1.990 7.2 4.2 1.7 86.4 81.3 0.94 4.5 1.3 23.3 15.1 0.3 85.0 0 0 0 0 0 0 0 120 80 3 3 17.0 18.0 6.8
0 33 48.0 1.6 18.7 17 72 18 10.2 2 5 5.0 0 0 6.0 10.240 7.0 4.5 1.6 71.1 61.0 0.86 8.5 12.0 22.3 28.6 0.2 100.0 0 0 0 0 0 0 0 110 80 7 10 18.0 16.0 10.0
0 35 52.0 1.5 23.1 13 74 20 11.1 2 4 15.0 1 1 2.0 1.990 2.2 0.5 4.3 76.2 73.7 0.97 2.5 3.3 40.4 24.0 0.3 115.0 0 0 0 0 0 0 0 110 70 3 3 18.0 18.0 7.0
0 35 42.0 1.5 18.7 11 78 18 10.8 2 5 12.0 0 1 300.5 1.990 1.7 0.3 5.6 71.1 61.0 0.86 3.1 1.7 32.9 15.0 0.5 108.0 0 0 1 1 0 0 0 120 80 3 10 16.0 21.0 7.0
0 37 62.0 1.5 27.6 11 74 20 10.7 2 4 13.0 0 0 316.5 1.990 8.1 3.3 2.5 111.8 109.2 0.98 1.7 0.9 10.9 24.3 0.3 107.0 0 1 0 1 0 1 0 110 80 6 6 19.0 19.0 7.5
0 36 54.3 1.5 24.1 11 78 20 10.0 2 5 12.0 0 0 2.0 1.990 7.4 4.0 1.9 81.3 76.2 0.94 2.1 4.9 20.8 17.9 0.2 138.0 0 1 0 0 0 0 0 110 70 1 1 16.0 19.0 9.1
1 46 54.0 1.5 24.0 13 74 20 14.0 2 4 16.0 1 1 856.1 1.990 2.9 0.4 7.7 76.2 71.1 0.93 1.1 16.6 8.1 23.7 0.3 92.0 1 0 0 0 0 1 0 120 70 20 18 18.0 17.0 7.8
0 24 57.0 1.6 22.3 17 70 20 11.2 2 5 3.0 0 0 2.0 1.990 1.7 0.4 4.0 81.3 76.2 0.94 4.0 17.6 26.0 44.8 0.7 100.0 1 0 0 0 0 0 0 120 70 8 10 18.0 19.0 8.4
0 44 56.0 1.4 28.6 13 72 18 11.8 2 5 3.0 0 0 42.6 1.990 2.8 0.1 18.7 96.5 91.4 0.95 1.0 4.6 10.9 16.9 0.7 82.0 0 0 0 0 1 0 0 110 80 9 5 19.0 19.0 6.0
1 23 72.0 1.7 24.9 13 72 18 10.6 5 7 5.0 0 0 121.0 1.990 2.1 0.6 3.7 106.7 101.6 0.95 1.0 5.5 16.3 17.0 0.2 92.0 0 1 1 0 0 1 0 120 70 8 10 16.0 18.0 9.3
0 21 55.0 1.6 21.5 16 72 18 11.5 2 5 3.0 0 0 2.0 1.990 8.6 2.5 3.4 101.6 96.5 0.95 1.8 2.2 17.2 13.8 0.2 100.0 0 0 0 0 0 0 0 120 80 1 3 7.0 19.0 8.5
0 32 65.0 1.7 22.5 16 72 18 10.5 2 5 6.0 0 0 171.8 1.990 7.4 2.9 2.6 96.5 91.4 0.95 3.0 18.9 21.0 30.0 0.5 100.0 0 0 0 0 0 1 0 110 70 9 7 18.0 18.0 9.0
0 31 69.0 1.6 27.0 15 72 18 11.0 2 5 4.0 0 0 2.0 1.990 6.5 2.3 2.8 116.8 111.8 0.96 1.4 10.0 19.2 22.9 0.3 92.0 0 0 0 1 0 0 0 110 80 10 8 20.0 17.0 7.0
0 34 50.0 1.5 22.2 15 72 18 10.0 2 5 8.0 0 0 2.0 104.870 3.2 2.7 1.2 81.3 71.1 0.87 3.9 16.0 7.2 21.7 0.2 92.0 0 0 1 0 0 0 0 100 70 12 10 19.0 16.0 9.2
0 27 59.0 1.5 26.2 11 72 18 11.5 2 5 2.0 0 0 320.1 1.990 6.0 3.9 1.5 91.4 86.4 0.95 2.2 5.4 35.2 24.2 0.5 92.0 0 0 0 0 0 0 0 110 70 5 8 20.0 21.0 9.8
0 24 56.0 1.5 24.9 17 72 18 10.5 2 5 4.0 0 0 31.8 1.990 7.7 3.3 2.3 76.2 71.1 0.93 3.9 3.7 27.4 19.7 0.4 92.0 0 0 0 0 0 0 0 110 70 5 4 19.0 20.0 5.9
0 28 53.0 1.6 20.7 16 72 18 11.8 2 5 5.0 1 0 2.0 1.990 5.6 3.8 1.5 71.1 66.0 0.93 2.1 5.5 23.0 32.0 0.4 100.0 0 0 1 0 0 0 0 110 80 4 4 19.0 16.0 6.5
1 27 50.0 1.7 17.3 13 72 18 12.0 4 2 7.0 0 0 632.2 1.990 4.6 2.2 2.1 66.0 63.5 0.96 0.9 7.2 23.1 37.9 0.4 100.0 0 1 0 0 1 0 0 120 70 18 20 20.0 19.0 7.8
0 40 78.0 1.6 30.5 15 74 20 11.2 2 5 14.0 0 0 212.8 1.990 9.1 3.2 2.8 121.9 116.8 0.96 2.1 2.5 25.0 23.4 0.2 92.0 0 1 0 0 1 0 0 120 80 8 10 19.0 21.0 8.0
0 29 52.0 1.5 23.1 13 72 18 11.8 2 5 8.0 1 0 157.7 1.990 7.1 3.3 2.2 71.1 63.5 0.89 2.8 18.5 16.3 19.0 0.6 140.0 1 0 1 1 0 0 0 110 80 6 5 17.5 16.5 10.0
1 36 60.0 1.5 26.7 15 72 18 10.2 4 7 8.0 0 0 2.1 1.990 5.7 1.0 5.7 106.7 101.6 0.95 2.1 7.7 39.4 26.1 0.2 110.0 1 0 0 0 1 0 0 110 80 6 9 17.0 22.0 7.6
1 27 70.0 1.7 24.2 13 72 18 11.5 2 5 4.0 1 2 2.0 1.990 4.3 2.0 2.1 111.8 106.7 0.95 3.6 0.2 9.8 21.8 0.5 107.0 1 0 0 0 0 0 0 120 80 9 10 21.0 23.0 6.5
0 27 48.0 1.6 18.7 13 72 18 10.7 2 5 2.0 0 0 2.0 1.990 8.9 4.6 1.9 66.0 61.0 0.92 4.3 0.6 19.5 24.3 0.2 100.0 0 1 0 0 0 0 0 110 70 2 3 16.0 19.0 7.8
0 32 60.0 1.6 23.4 16 74 22 10.5 4 7 4.0 1 1 84.0 1.990 3.3 2.6 1.3 106.7 101.6 0.95 1.5 2.5 11.2 12.7 0.2 107.0 0 1 0 0 0 0 0 120 80 5 3 6.8 12.0 6.8
0 22 62.0 1.6 24.2 15 74 22 11.1 2 4 3.0 0 0 18.8 1.990 4.4 2.7 1.6 111.8 106.7 0.95 2.1 3.7 28.8 21.4 0.2 115.0 0 0 0 0 1 0 0 120 80 10 9 21.0 18.0 6.2
0 41 58.0 1.5 25.8 13 72 20 10.5 2 6 6.0 0 0 2.0 1.990 9.6 3.7 2.6 106.7 101.6 0.95 2.5 0.8 16.2 31.0 0.2 92.0 1 0 0 0 0 1 0 120 80 1 0 19.0 14.0 9.7
0 32 52.0 1.6 20.3 13 70 24 10.8 2 6 4.0 0 0 2.0 1.990 1.4 0.4 3.6 76.2 71.1 0.93 5.0 2.3 14.9 19.3 0.2 108.0 0 0 0 0 0 1 0 120 80 3 1 18.0 17.0 7.3
0 38 61.0 1.6 23.8 15 72 20 12.3 2 5 10.0 0 0 270.5 1.990 9.3 2.5 3.7 96.5 86.4 0.90 1.3 1.0 19.3 30.5 0.3 100.0 0 0 0 0 0 0 0 120 80 10 11 21.0 12.0 4.8
0 34 70.0 1.6 27.3 11 72 22 10.5 2 5 8.0 0 0 2.0 1.990 6.9 2.1 3.3 121.9 111.8 0.92 1.1 0.7 39.3 11.3 0.2 92.0 0 0 0 0 0 0 0 120 70 2 0 21.0 16.0 8.5
0 39 56.0 1.6 21.9 12 72 22 10.8 2 4 13.0 0 0 667.1 1.990 10.3 5.1 2.0 81.3 71.1 0.87 1.5 6.3 32.1 23.4 0.2 92.0 0 0 0 0 0 0 0 130 80 9 7 17.0 18.0 7.0
1 26 53.5 1.6 20.9 14 70 18 10.6 4 7 3.0 0 0 572.9 5.810 3.7 4.7 0.8 71.1 66.0 0.93 1.4 19.6 29.6 32.1 0.4 100.0 0 0 0 0 1 0 0 110 80 8 10 18.0 18.0 10.3
0 24 48.0 1.6 18.7 14 70 18 11.0 2 5 4.0 0 0 615.3 1.990 3.7 9.4 0.4 76.2 71.1 0.93 1.7 18.2 33.9 26.9 0.3 92.0 0 0 0 0 0 0 0 110 80 7 7 20.0 19.0 8.2
0 26 80.0 1.6 31.2 18 70 18 10.6 2 5 4.0 0 0 2.0 1.990 7.1 3.5 2.0 121.9 111.8 0.92 17.2 7.6 39.0 32.7 0.2 92.0 0 0 0 0 0 0 0 110 80 7 9 13.0 17.5 9.6
0 35 50.0 1.6 19.5 17 72 16 11.0 2 5 8.0 0 1 2.0 1.990 10.1 1.8 5.6 71.1 66.0 0.93 1.1 1.7 5.3 36.6 0.2 92.0 0 0 0 0 0 0 0 110 70 1 0 17.5 10.0 6.7
0 30 63.2 1.6 24.7 15 72 18 10.8 2 5 4.0 1 1 80.1 1.990 5.1 2.8 1.8 86.4 81.3 0.94 2.0 5.6 21.1 23.0 0.2 108.0 1 0 0 0 0 0 0 110 70 9 7 19.0 18.0 8.2
0 36 54.0 1.5 24.0 13 74 20 10.8 2 6 8.0 0 0 2.0 1.990 12.0 2.8 4.3 76.2 71.1 0.93 2.9 3.7 96.4 22.5 0.2 92.0 0 0 0 0 0 0 0 110 80 1 0 18.0 9.0 7.3
0 27 50.0 1.5 22.2 15 74 20 12.0 4 2 2.0 0 0 292.9 1.990 4.4 4.3 1.0 71.1 66.0 0.93 2.5 5.2 38.9 22.4 0.2 115.0 0 0 0 0 1 0 0 110 70 7 6 18.0 16.0 11.5
1 23 82.0 1.7 28.4 13 80 20 10.2 4 7 2.0 0 0 2.0 1.990 4.0 4.3 0.9 121.9 116.8 0.96 1.7 20.0 20.7 17.4 0.4 108.0 1 1 1 1 1 1 0 120 70 9 10 19.0 18.0 6.9

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Once more, I generated a DataExplorer histogram to inspect the impact of the alterations on the distribution. While more variables have been included in the distribution, there isn’t a consistent pattern; each variable demonstrates variations, displaying either a normal distribution, right-skewed, left-skewed, or no discernible pattern.

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The correlation plot below is measuring the degree of linear relationship within the dataset. The values in which this is measured falls between -1 and +1, with +1 being a strong positive correlation and -1 a strong negative correlation. The darker the dot the more strongly correlated (whether positive or negative). Based on the findings below, there aren’t many variables that exhibit a notably strong positive or negative correlation, although some variables do fall within these categories to some extent.

Some notable correlations are as follows:

  • Positive correlations between: BMI and Weight, Weight_gain, Hair_growth, Skin_darkeningand PCOS and Follicle_NoL, Follicle_NoR, and PCOS to name a few.

  • Negative correlations between: Waist_Hip_Ratio and Hip, AMH_ngmL and Age_yrs, and Follicle_NoR, Follicle_NoL andAge_yrs.

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Outliers, while sometimes seen as anomalies or errors, hold significant importance in data analysis for various reasons. They can substantially influence statistical measures, potentially skewing interpretations of central tendency (mean, median, mode) and dispersion (variance, standard deviation). Their presence might distort the true characteristics of the data distribution. Outliers can adversely affect the performance of predictive models. Many machine learning algorithms are sensitive to outliers, leading to biased predictions or reduced model accuracy. Identifying and handling outliers appropriately is crucial for robust model building. Recognizing outliers aids in improving data quality by either correcting errors or understanding exceptional cases in the dataset. If handled properly, outliers can enhance our understanding of the dataset, improve model performance, and contribute to more accurate and meaningful insights. Detecting and managing outliers is essential for maintaining the integrity and reliability of data-driven analyses. With the help of boxplots we can identify outliers across a complete dataset.

According to the visualizations provided, a handful of outliers are evident. Considering the dataset’s relatively small size, I’ve chosen to retain these outliers to ensure the inclusion of every individual data point as it represents natural variations in the population.

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Further visualizations:

Before diving into the model-building phase of this project, let’s further visualize the variables with a series of scatterplots, histograms and bar charts.

  • There is a wide range of distribution between the age of these women most falling between 23 to 38 years old. Similarly, weight also has a wide range of distribution.

  • Most of the women in this study are between 1.5 - 1.6 meters tall (4’9” - 5’2”).

  • The distribution of years married also varies where the majority of the women fall between the first 10 years.

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Various physiological and hormonal patterns within the dataset and related to PCOS show that:

  • The age and weight of the women are distributed evenly throughout as opposed to being clustered in a certain age and weight group.

  • Hip to waist counts increase for those with and without PCOS.

  • Cycle length seems to be very consistent regardless of PCOS diagnosis.

  • Women with PCOS experience more irregular periods with a few with no PCOS do as well.

  • With or without PCOS women’s BMI fluctuates in women of all ages.

  • The distribution of number of follicles is greater for those women without PCOS.

  • The distribution of size in follicles left or right don’t differ as greatly as I thought for women with and without PCOS.

  • The variance in endometrial thickness between women with PCOS (thinner) and those without PCOS (thicker) evolves as the menstrual cycle progresses. This discrepancy arises due to hormonal imbalances. In women affected by PCOS, the absence of regular menstrual cycles leads to an unaltered endometrial lining, contrasting with the changes observed in women without the condition.

  • Pregnancy hormones are clustered with the exceptions of some outliers which were initially noted.

  • Blood pressure levels, Pulse rate and Respiration rate for women with and without PCOS are in normal range.

  • The quantity of eggs and the development of follicles show similar patterns between women self-reporting PCOS and those without, yet there is no discernible correlation between them.

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Out of 541 women:

  • 32.72% reported to have PCOS

  • 38.08% of women reported to being pregnant

  • 37.71% reported to experience weight gain

  • 27.36% reported to experience hair growth

  • 30.68% experience skin darkening

  • 45.29% reported to experience hair loss

  • 48.98% reported to experience pimples

  • 51.57% reported to have fast food

  • 24.77% reported to exercise regularly

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Taking into account the presence of PCOS within the study:

  • There’s prevalence of O+, B+, and A+ blood types among the majority of women involved. Furthermore, women possessing blood types A+, B+, and O+ reported a higher incidence of PCOS compared to those with blood types A-, B-, O-, AB+, and AB-.

  • Being that PCOS and infertility are linked, there are 11.83% of women who reported to being pregnant with PCOS.

  • 22.37% of women reported to experience weight gain with PCOS.

  • 18.67% of women reported to experience hair growth with PCOS.

  • 20.33% of women reported to experience skin darkening with PCOS.

  • 18.85% of women reported to experience hair loss with PCOS.

  • 22.74% of women reported to experience pimples with PCOS.

  • 25.13% of women reported to consume fast food with PCOS.

  • 9.43% of women reported to regularly exercise with PCOS.

  • Not surprised to see how fluctuated the cycle length is especially for women without PCOS since they tend to have a more regular cycle.

  • Abortions were also included in this study but there’s no evidence that it’s influenced by PCOS; more women without PCOS experience 1 or more abortions.

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Within hormonal markers:

  • Vitamin D3 levels are spread out for those who reported with or without PCOS.

  • FSH/LH levels are right skewed for those who reported with or without PCOS.

  • TSH levels are also spread out for women who reported to have PCOS and those who don’t.

  • Hemoglobin levels seem to be higher for those who reported to have PCOS.

  • PRL levels (prolactin in the blood) are consistent at 1 or 2 for those with or without PCOS.

  • RBS (random glucose) is fairly distributed with those with and without PCOS.

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Model Building:

As previously mentioned, I’ve decided for the prediction anaylsis to do Decision Tree (rpart), Random Forest (randomForest), Gradient Boosting Machines (xgboost or gbm), Support Vector Machines (e1071), Neural Networks (neuralnet), and K-Nearest Neighbors (kknn or class).

Decision Tree (rpart): Decision trees recursively split the dataset based on features to make decisions. They represent a tree-like structure where nodes correspond to features, branches represent decisions, and leaves denote outcomes. R’s rpart package builds decision trees using the Recursive Partitioning and Regression Trees algorithm.

Random Forest (randomForest): Random Forest is an ensemble learning method that constructs multiple decision trees and merges their predictions to improve accuracy and reduce overfitting. The randomForest package in R implements this ensemble method, creating a collection of decision trees and aggregating their predictions.

Gradient Boosting Machines (xgboost or gbm): Gradient Boosting Machines (GBMs) create an ensemble of weak learners (typically decision trees) sequentially, where each new tree corrects the errors made by the previous one. R provides packages like xgboost and gbm to implement gradient boosting, allowing users to build efficient and optimized boosting models.

Support Vector Machines (e1071): Support Vector Machines are supervised learning models used for classification or regression tasks. They find a hyperplane that best separates data into different classes while maximizing the margin. The e1071 package in R includes SVM implementations for classification and regression tasks.

Neural Networks (neuralnet): Neural Networks are a set of algorithms designed to recognize patterns. They consist of interconnected nodes (neurons) organized in layers and can model complex relationships in data. The neuralnet package in R allows users to create and train neural networks for various tasks like regression and classification.

K-Nearest Neighbors (kknn or class): K-Nearest Neighbors (KNN) is a simple, instance-based learning algorithm. It classifies new data points based on majority voting from the k-nearest data points in the training set. R provides packages like kknn or class to implement KNN algorithms for both classification and regression tasks.

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We start by splitting the dataset into training and validation sets for machine learning models. The training set is used to train the model, while the validation set helps evaluate its performance. The 75:25 ratio used strikes a balance between having enough data to train the model effectively and having a substantial validation set for robust evaluation.

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Decision Tree (rpart):

The initial decision tree constructed is based on PCOS against the entire dataset. Employing a 75:25 cross-validation approach to divided the data I created the decision tree. The output is below:

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Then we test the model using the validation dataset. The results are seen in the confusion matrix and statistics output.

The confusion matrix shows the following layout:

  • True Negative (TN) = 88: Actual class 0 (negative) correctly predicted as 0.
  • False Positive (FP) = 13: Actual class 0 incorrectly predicted as 1 (positive).
  • False Negative (FN) = 6: Actual class 1 incorrectly predicted as 0.
  • True Positive (TP) = 28: Actual class 1 correctly predicted as 1.

Accuracy and Confidence Interval (CI):

Accuracy: 85.93% - The proportion of correctly classified instances out of the total instances. 95% Confidence Interval: The range within which the true accuracy is likely to lie.

Other Statistics:

  • No Information Rate (NIR): The accuracy rate if the model simply predicted the majority class for all instances.

Interpretation:

  • The model has a relatively high accuracy (85.93%), suggesting it correctly predicts classes in the dataset.
  • Sensitivity (True Positive Rate) is high (93.62%), indicating that the model is good at identifying actual positive instances.
  • Specificity (True Negative Rate) is moderate (68.29%), suggesting a decent ability to identify actual negative instances.
  • The positive predictive value (Precision) is 87.13%, indicating the proportion of correctly predicted positive instances out of all positive predictions.

There might be a class imbalance issue, considering the differences in sensitivity and specificity. Overall, while the model shows relatively good performance, further analysis might be required to understand its behavior, especially if sensitivity or specificity is more critical for the specific application or domain.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 88 13
##          1  6 28
##                                          
##                Accuracy : 0.8593         
##                  95% CI : (0.789, 0.9131)
##     No Information Rate : 0.6963         
##     P-Value [Acc > NIR] : 8.686e-06      
##                                          
##                   Kappa : 0.6504         
##                                          
##  Mcnemar's Test P-Value : 0.1687         
##                                          
##             Sensitivity : 0.9362         
##             Specificity : 0.6829         
##          Pos Pred Value : 0.8713         
##          Neg Pred Value : 0.8235         
##              Prevalence : 0.6963         
##          Detection Rate : 0.6519         
##    Detection Prevalence : 0.7481         
##       Balanced Accuracy : 0.8095         
##                                          
##        'Positive' Class : 0              
##

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Upon examining the individual contributions of each variable. It’s evident that Follicle_NoL, Follicle_NoR, Hair_growth, Skin_darkening, and Weight_gain, hold significant influence within the dataset. I expected a wider range of influential bloodwork-related tests beyond those presented in this outcome.

Overall
AMH_ngmL 7.472686
Avg_F_sizeL_mm 6.214255
Avg_F_sizeR_mm 5.786395
Cycle_length_days 8.214096
Cycle_RI 5.001685
Endometrium_mm 2.660754
Fast_food 16.064907
Follicle_NoL 87.518272
Follicle_NoR 100.167907
FSH_LH 3.339002
Hair_growth 47.133117
Hb_gdl 1.771648
IbetaHCG_mIUmL 2.443609
IIbetaHCG_mIUmL 2.586655
LH_mIUmL 9.505773
Pimples 4.456912
Skin_darkening 50.260647
TSH_mIUmL 5.780381
Waist 2.443609
Waist_Hip_Ratio 2.889376
Weight_gain 67.058844
Age_yrs 0.000000
Weight 0.000000
Height 0.000000
BMI 0.000000
Blood_Group 0.000000
Pulse_rate_bpm 0.000000
RR_breaths_min 0.000000
Married_yrs 0.000000
Pregnant 0.000000
No_of_abortions 0.000000
FSH_mIUmL 0.000000
Hip 0.000000
PRL_ngmL 0.000000
Vit_D3_ngmL 0.000000
PRG_ngmL 0.000000
RBS_mgdl 0.000000
Hair_loss 0.000000
Reg_Exercise 0.000000
BP_Systolic_mmHg 0.000000
BP_Diastolic_mmHg 0.000000

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The model’s accuracy stands at 85.93%, which is commendable. However, considering the variable contributions, I’m planning to build a second decision tree by removing the variables with lower contributions.

x
Accuracy 0.8592593

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Second Decision Tree:

The second decision tree is based off the variables with the highest contribution: Follicle_NoL, Follicle_NoR, Hair_growth, Skin_darkening, Weight_gain and the target variable PCOS. I also created a second cross validation data set that include the 6 variables chosen. The results are below:

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Same as before, we create the confusion matrix and statistics for the second decision tree using the validation data:

Interpretation:

  • The model displays high accuracy (91.85%) in correctly predicting classes.
  • Sensitivity (True Positive Rate) is very high (98.94%), indicating an excellent ability to identify actual positive instances.
  • Specificity (True Negative Rate) is moderate (75.61%), suggesting a decent ability to identify actual negative instances.
  • Positive Predictive Value (Precision) is 90.29%, meaning that around 90.29% of the predicted positive instances are actually positive.

The model seems to perform well in this dataset, with high sensitivity and specificity, indicating robustness in classifying both positive and negative instances effectively.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 93 10
##          1  1 31
##                                           
##                Accuracy : 0.9185          
##                  95% CI : (0.8589, 0.9586)
##     No Information Rate : 0.6963          
##     P-Value [Acc > NIR] : 3.627e-10       
##                                           
##                   Kappa : 0.7946          
##                                           
##  Mcnemar's Test P-Value : 0.01586         
##                                           
##             Sensitivity : 0.9894          
##             Specificity : 0.7561          
##          Pos Pred Value : 0.9029          
##          Neg Pred Value : 0.9688          
##              Prevalence : 0.6963          
##          Detection Rate : 0.6889          
##    Detection Prevalence : 0.7630          
##       Balanced Accuracy : 0.8727          
##                                           
##        'Positive' Class : 0               
##

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Let’s examine the impact of each variable within the context of the second dataset. There’s a noticeable shift in the overall contribution levels among the variables.

Overall
Follicle_NoL 83.31931
Follicle_NoR 90.19890
Hair_growth 82.08275
Skin_darkening 61.41410
Weight_gain 58.35553

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Ultimately, we observe that the accuracy has increased to 91.85% compared to the initial decision tree.

x
Accuracy 0.9185185

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Random Forest (randomForest):

The third model created is a random forest model for the entire dataset. Following similar steps as the decision tree, the model was trained using the entire dataset. The model’s performance, as indicated by the confusion matrix, demonstrates a limited accuracy and predictive power. The Kappa statistic reflects weak agreement, and the sensitivity and specificity are relatively imbalanced. This suggests that the model might require further refinement or alternative approaches to improve its predictive capability.

## 
## Call:
##  randomForest(formula = PCOS ~ ., data = rf_train) 
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 6
## 
##         OOB estimate of  error rate: 10.34%
## Confusion matrix:
##     0   1 class.error
## 0 255  13  0.04850746
## 1  29 109  0.21014493
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 70 26
##          1 26 13
##                                           
##                Accuracy : 0.6148          
##                  95% CI : (0.5272, 0.6972)
##     No Information Rate : 0.7111          
##     P-Value [Acc > NIR] : 0.9938          
##                                           
##                   Kappa : 0.0625          
##                                           
##  Mcnemar's Test P-Value : 1.0000          
##                                           
##             Sensitivity : 0.7292          
##             Specificity : 0.3333          
##          Pos Pred Value : 0.7292          
##          Neg Pred Value : 0.3333          
##              Prevalence : 0.7111          
##          Detection Rate : 0.5185          
##    Detection Prevalence : 0.7111          
##       Balanced Accuracy : 0.5312          
##                                           
##        'Positive' Class : 0               
##

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The plot below demonstrates the variable importance of the entire dataset. Follicle_NoR, FollicleNoL and Hair_growth are the top 3 variables that play a significant role in the classification of PCOS presence.

Numerically, we can see the same result below:
Overall
Age_yrs 3.9555940
Weight 3.5133227
Height 0.7509959
BMI 4.1656865
Blood_Group 1.1969776
Pulse_rate_bpm 1.3839607
RR_breaths_min 0.8132189
Hb_gdl 3.2493027
Cycle_RI 4.6758889
Cycle_length_days 3.9395655
Married_yrs 3.0235019
Pregnant 0.4524712
No_of_abortions 0.5035535
IbetaHCG_mIUmL 2.9770701
IIbetaHCG_mIUmL 1.9619500
FSH_mIUmL 2.7633100
LH_mIUmL 4.5357367
FSH_LH 4.7013005
Hip 3.3652777
Waist 2.7766286
Waist_Hip_Ratio 2.6641628
TSH_mIUmL 3.1287353
AMH_ngmL 5.4699959
PRL_ngmL 3.0907356
Vit_D3_ngmL 3.2789071
PRG_ngmL 1.9663800
RBS_mgdl 2.5487802
Weight_gain 7.2095545
Hair_growth 11.3787522
Skin_darkening 9.6398941
Hair_loss 1.0092750
Pimples 3.8558985
Fast_food 3.9632944
Reg_Exercise 0.9608261
BP_Systolic_mmHg 0.8078958
BP_Diastolic_mmHg 0.4509238
Follicle_NoL 22.5222225
Follicle_NoR 34.0208589
Avg_F_sizeL_mm 2.8505388
Avg_F_sizeR_mm 3.0127765
Endometrium_mm 2.9518284

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The model’s accuracy stood at 62.96%, demonstrating a performance that was not particularly strong.

## Error: `data` and `reference` should be factors with the same levels.
## Error in eval(expr, envir, enclos): object 'accuracy_rf' not found

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Second Random Forest Model:

The second random forest model was created using the 6 variables Follicle_NoL, Follicle_NoR, Hair_growth, Skin_darkening, Weight_gain and the target variable PCOS. After training this model, the following confusion matrix was the result.

## 
## Call:
##  randomForest(formula = PCOS ~ Follicle_NoR + Follicle_NoL + Weight_gain +      Skin_darkening + Hair_growth, data = rf_train2) 
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 2
## 
##         OOB estimate of  error rate: 10.34%
## Confusion matrix:
##     0   1 class.error
## 0 253  13  0.04887218
## 1  29 111  0.20714286
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 91  6
##          1  7 31
##                                          
##                Accuracy : 0.9037         
##                  95% CI : (0.841, 0.9477)
##     No Information Rate : 0.7259         
##     P-Value [Acc > NIR] : 3.16e-07       
##                                          
##                   Kappa : 0.76           
##                                          
##  Mcnemar's Test P-Value : 1              
##                                          
##             Sensitivity : 0.9286         
##             Specificity : 0.8378         
##          Pos Pred Value : 0.9381         
##          Neg Pred Value : 0.8158         
##              Prevalence : 0.7259         
##          Detection Rate : 0.6741         
##    Detection Prevalence : 0.7185         
##       Balanced Accuracy : 0.8832         
##                                          
##        'Positive' Class : 0              
##

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From the random forest model we created, we can create a variable importance plot which shows each variable and how important it is in classifying the data. From the plot below we note that Follicle_NoR and Follicle_NoL are among the top variables that play a significant role in the classification of having or not having PCOS .

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Numerically, we can see the same result below:
Overall
Follicle_NoR 66.76882
Follicle_NoL 41.73742
Weight_gain 11.61630
Skin_darkening 15.09906
Hair_growth 16.99740

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Lastly, I checked against the validation data the accuracy of the second model with the results of 90.37% accuracy which surpasses the first random forest model.

##  Accuracy 
## 0.9037037

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Gradient Boosting Machines (xgboost or gbm):

The fifth model is the start of the Gradient Boosting Machines model which is created using the entire dataset with the target variable PCOS.

In a GBM model, the “rel.inf” typically denotes the relative importance of each feature in predicting the target variable. Follicle_NoR is identified as the most crucial variable in the model, followed by others that contribute to a lesser extent.

## Using 100 trees...

##                                 var     rel.inf
## Follicle_NoR           Follicle_NoR 56.53789574
## Follicle_NoL           Follicle_NoL  9.31124699
## Weight_gain             Weight_gain  7.25056688
## Hair_growth             Hair_growth  5.04710013
## Skin_darkening       Skin_darkening  4.68444934
## Cycle_RI                   Cycle_RI  3.02919386
## Fast_food                 Fast_food  2.59491831
## AMH_ngmL                   AMH_ngmL  2.48329641
## LH_mIUmL                   LH_mIUmL  1.95414445
## Married_yrs             Married_yrs  0.98595862
## Pimples                     Pimples  0.98012441
## FSH_LH                       FSH_LH  0.76664354
## IbetaHCG_mIUmL       IbetaHCG_mIUmL  0.75629336
## Weight                       Weight  0.52172697
## TSH_mIUmL                 TSH_mIUmL  0.48025428
## Cycle_length_days Cycle_length_days  0.45544240
## Avg_F_sizeR_mm       Avg_F_sizeR_mm  0.36710466
## FSH_mIUmL                 FSH_mIUmL  0.35002808
## Endometrium_mm       Endometrium_mm  0.32967917
## Hip                             Hip  0.21306422
## Hair_loss                 Hair_loss  0.17423515
## PRL_ngmL                   PRL_ngmL  0.12779690
## Waist                         Waist  0.12673974
## Waist_Hip_Ratio     Waist_Hip_Ratio  0.12342779
## BMI                             BMI  0.12267190
## Avg_F_sizeL_mm       Avg_F_sizeL_mm  0.08242395
## Vit_D3_ngmL             Vit_D3_ngmL  0.05613253
## Age_yrs                     Age_yrs  0.04379413
## RBS_mgdl                   RBS_mgdl  0.04364608
## Height                       Height  0.00000000
## Blood_Group             Blood_Group  0.00000000
## Pulse_rate_bpm       Pulse_rate_bpm  0.00000000
## RR_breaths_min       RR_breaths_min  0.00000000
## Hb_gdl                       Hb_gdl  0.00000000
## Pregnant                   Pregnant  0.00000000
## No_of_abortions     No_of_abortions  0.00000000
## IIbetaHCG_mIUmL     IIbetaHCG_mIUmL  0.00000000
## PRG_ngmL                   PRG_ngmL  0.00000000
## Reg_Exercise           Reg_Exercise  0.00000000
## BP_Systolic_mmHg   BP_Systolic_mmHg  0.00000000
## BP_Diastolic_mmHg BP_Diastolic_mmHg  0.00000000

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We create the confusion matrix and statistics for the first gbm. The model exhibits strong overall accuracy, substantial agreement (Kappa), and good performance in correctly identifying positive and negative classes, as indicated by sensitivity, specificity, and predictive values.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 90 11
##          1  4 30
##                                           
##                Accuracy : 0.8889          
##                  95% CI : (0.8234, 0.9365)
##     No Information Rate : 0.6963          
##     P-Value [Acc > NIR] : 1.001e-07       
##                                           
##                   Kappa : 0.724           
##                                           
##  Mcnemar's Test P-Value : 0.1213          
##                                           
##             Sensitivity : 0.9574          
##             Specificity : 0.7317          
##          Pos Pred Value : 0.8911          
##          Neg Pred Value : 0.8824          
##              Prevalence : 0.6963          
##          Detection Rate : 0.6667          
##    Detection Prevalence : 0.7481          
##       Balanced Accuracy : 0.8446          
##                                           
##        'Positive' Class : 0               
##

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The accuracy of this model is 88.89% which is good but a second model will be created with the selected variables.

## Accuracy: 0.8888889

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Second GBM:

The second GBM is based off the variables: Follicle_NoL, Follicle_NoR, Hair_growth, Skin_darkening, Weight_gain and the target variable PCOS. The results are below:

Similar to before: Follicle_NoR is identified as the most crucial variable in the model but with the decrease rel.inf of 55.111884.

## Using 100 trees...

##                           var   rel.inf
## Follicle_NoR     Follicle_NoR 55.111884
## Follicle_NoL     Follicle_NoL 17.216462
## Skin_darkening Skin_darkening  9.828913
## Hair_growth       Hair_growth  9.112817
## Weight_gain       Weight_gain  8.729924

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The model also exhibits a reasonably good accuracy and agreement between actual and predicted classes. It shows higher sensitivity than the first model but relatively lower specificity. The model’s overall performance is moderately robust but might have some limitations in correctly identifying negative instances (class 0).

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 90 13
##          1  3 29
##                                           
##                Accuracy : 0.8815          
##                  95% CI : (0.8147, 0.9307)
##     No Information Rate : 0.6889          
##     P-Value [Acc > NIR] : 1.369e-07       
##                                           
##                   Kappa : 0.7042          
##                                           
##  Mcnemar's Test P-Value : 0.02445         
##                                           
##             Sensitivity : 0.9677          
##             Specificity : 0.6905          
##          Pos Pred Value : 0.8738          
##          Neg Pred Value : 0.9063          
##              Prevalence : 0.6889          
##          Detection Rate : 0.6667          
##    Detection Prevalence : 0.7630          
##       Balanced Accuracy : 0.8291          
##                                           
##        'Positive' Class : 0               
##

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The accuracy observed in this GBM model is slightly reduced compared to the initial one, achieving an 88.15% accuracy rate.

## Accuracy: 0.8814815

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Support Vector Machines (e1071):

To start the SVM algorithm and following the similar criteria as the previous models by setting up the cross validation set-up, create the prediction, confusion matrix and lastly it’s accuracy.

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The confusion matrix below shows sensitivity (true positive rate) for class 0 is high at 95.74%, indicating the model’s effectiveness in identifying instances of class 0. Specificity for class 1 is 80.49%, which is reasonably good. The balanced accuracy, which considers both sensitivity and specificity, is at 88.12%, demonstrating a good overall performance in distinguishing between both classes. Overall, based on these metrics, the confusion matrix generally portrays a good performance of the model.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 90  8
##          1  4 33
##                                           
##                Accuracy : 0.9111          
##                  95% CI : (0.8499, 0.9532)
##     No Information Rate : 0.6963          
##     P-Value [Acc > NIR] : 1.675e-09       
##                                           
##                   Kappa : 0.7839          
##                                           
##  Mcnemar's Test P-Value : 0.3865          
##                                           
##             Sensitivity : 0.9574          
##             Specificity : 0.8049          
##          Pos Pred Value : 0.9184          
##          Neg Pred Value : 0.8919          
##              Prevalence : 0.6963          
##          Detection Rate : 0.6667          
##    Detection Prevalence : 0.7259          
##       Balanced Accuracy : 0.8812          
##                                           
##        'Positive' Class : 0               
##

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A Principal Component Analysis (PCA) plot visualizes the data in a reduced dimensional space based on the principal components extracted from the original dataset. The visualization illustrates a clustering of data points primarily in the lower left section of the chart, representing both classes (individuals with PCOS and those without). This clustering is a result of the inherent variance within the data and may not be specifically optimized for distinct separation between the classes.

## Error in svd(x, nu = 0, nv = k): infinite or missing values in 'x'
## Error in predict(pca_model, train): object 'pca_model' not found
## Error in plot(train_pca[, 1], train_pca[, 2], col = train$PCOS, main = "Fig.17 - PCA plot for SVM 1 model"): object 'train_pca' not found

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Within the SVM, there were 98 instances have been predicted as class 0 and 37 instances have been predicted as class 1.

##  0  1 
## 98 37

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The accuracy for the first model is 91.11%.

##  Accuracy 
## 0.9111111

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Second SVM:

The second SVM model was constructed following the same methodology as the initial model, utilizing the identical selection of the six variables employed thus far.

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The confusion matrix for the second SVM suggest that the model performs well in classifying instances, demonstrating high accuracy, sensitivity, specificity, and precision for the predicted classes. The Kappa statistic also indicates substantial agreement between the actual and predicted classes.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 94  7
##          1  4 30
##                                           
##                Accuracy : 0.9185          
##                  95% CI : (0.8589, 0.9586)
##     No Information Rate : 0.7259          
##     P-Value [Acc > NIR] : 2.136e-08       
##                                           
##                   Kappa : 0.7899          
##                                           
##  Mcnemar's Test P-Value : 0.5465          
##                                           
##             Sensitivity : 0.9592          
##             Specificity : 0.8108          
##          Pos Pred Value : 0.9307          
##          Neg Pred Value : 0.8824          
##              Prevalence : 0.7259          
##          Detection Rate : 0.6963          
##    Detection Prevalence : 0.7481          
##       Balanced Accuracy : 0.8850          
##                                           
##        'Positive' Class : 0               
##

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The plot for the second SVM model shows a similar pattern as the first SVM plot.

## Error in predict(pca_model, train): object 'pca_model' not found
## Error in plot(train_pca2[, 1], train_pca2[, 2], col = svm_train2$PCOS, : object 'train_pca2' not found
##   0   1 
## 101  34

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The second model demonstrated enhanced performance, achieving an accuracy of 91.85%.

##  Accuracy 
## 0.9185185

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Neural Networks (neuralnet):

Utilizing the neuralnet package in R I created the neural network models. The neural network is constructed to predict the target variable PCOS using all available predictors. The model is then trained using the training dataset using hidden argument that help specify the architecture of the neural network. These included setting up two hidden layers with 12 and 8 neurons, respectively. The stepmax parameter determines the maximum number of iterations allowed during training, here set to 20000 to ensure the model has enough iterations to learn from the data and optimize its parameters.

For context, neural network plots are composed of:

  • The left-most nodes (i.e. input nodes) are the raw data variables used in the model.

  • The arrows in black (and associated numbers) are the weights which you can think of as how much that variable contributes to the next node. The blue lines are the bias weights which are an additional parameter introduced to each neuron and serves as an offset or intercept term, contributing to the ability of the network to fit more complex patterns in the data.

  • The middle nodes (i.e. anything between the input and output nodes) are your hidden nodes. This is where the image analogy helps. Each of these nodes constitute a component that the network is learning to recognize.

  • The far-right (output node(s)) node is the final output of the neural network.

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## Error: the error derivative contains a NA; varify that the derivative function does not divide by 0 (e.g. cross entropy)

Although not displayed when knitted, the first neural network plot produced is seen below:

Fig. 19 - Neural Network 1
Fig. 19 - Neural Network 1

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I tried to recreated the neural network once more with the same data only this time, the parameters included 2 hidden layers with the first hidden layer containing 4 neurons and the second hidden layer containing 1 neuron. The stepmax parameter determines the maximum number of iterations allowed during training, here set to 10000 to ensure the model has enough iterations to learn from the data and optimize its parameters. Results are seen below:

## Error in plot(nn_model2, rep = "best"): object 'nn_model2' not found

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PCOS table displaying the predicted outcomes, where the highest count of predictions corresponds to the absence of PCOS.

## Error in predict(nn_model2, valid): object 'nn_model2' not found
## Error in as.matrix(m): object 'pred' not found
## Error in table(valid$PCOS, prediction_label): object 'prediction_label' not found

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The accuracy of the first nn_model was of 30.37%. The recreated nn_model2 accuracy was 72.59% which is relatively higher but still hoping this could improve in the third model.

## Error in as.matrix(m): object 'pred' not found
## Error in sum(check): invalid 'type' (closure) of argument
## Error in eval(expr, envir, enclos): object 'nn_accuracy' not found

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Second Neural Network:

For the second neural network model I am also using the neuralnet package. Similar to the first model, I created the model by specifying the variables from pcos_cleaned2 which included Follicle_No(L), Follicle_No(R), Hair_growth, Skin_darkening, Weight_gain, and the target variable PCOS and setting the parameters to 1 hidden layer containing 2 neurons and an output layer with 1 neuron.

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The plot below is the output of the model created:

In the context of a neuralnet plot displaying an error of 13.733636 and steps of 2518, this information typically pertains to the training progress of a neural network model and provides insights into the model’s learning behavior.

  • An error value of 13.733636 suggests the current level of model error or loss at the 2518th step/iteration during training.

  • Each step involves adjusting the model’s weights and biases to reduce the error, moving closer towards an optimal solution. Higher step counts usually mean more training iterations were required for the network to converge.

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Below is a table displaying the predicted outcomes for the PCOS status, where the highest count of predictions corresponds to the absence of PCOS.

##    prediction_label2
##      0
##   0 94
##   1 41

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Lastly, we check the accuracy using the validation data by first converting actual categorical values into numerical ones and compare them with predicted values. The neuralnet accuracy is 30.37% which is oddly similar to the accuracy of the first model that couldn’t be reproduced.

## [1] 0.3037037

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K-Nearest Neighbors (kknn or class):

To build the k-Nearest Neighbors model I prepared the data by removing rows with missing values, although there were none, the code wouldn’t run without it. After setting a seed for reproducibility I trained a kNN model using the training data and predict the ‘PCOS’ variable for the validation dataset. The kNN algorithm allows tweaking the number of neighbors (k) to impact the outcome. Here, it was fixed at 5; as a result, the accuracy achieved was 65.19%.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 84 33
##          1 14  4
##                                           
##                Accuracy : 0.6519          
##                  95% CI : (0.5651, 0.7317)
##     No Information Rate : 0.7259          
##     P-Value [Acc > NIR] : 0.97658         
##                                           
##                   Kappa : -0.0414         
##                                           
##  Mcnemar's Test P-Value : 0.00865         
##                                           
##             Sensitivity : 0.8571          
##             Specificity : 0.1081          
##          Pos Pred Value : 0.7179          
##          Neg Pred Value : 0.2222          
##              Prevalence : 0.7259          
##          Detection Rate : 0.6222          
##    Detection Prevalence : 0.8667          
##       Balanced Accuracy : 0.4826          
##                                           
##        'Positive' Class : 0               
## 

## [1] 0.6518519

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Second kNN:

For the second kNN, the same steps were takes as the first model with the exception of using the 6 variables of Follicle_NoR, Follicle_NoL, Weight_gain, Skin_darkening, Hair_growth and target variable PCOS. The result, an accuracy of 88.15% was achieved, which is much higher than the first model.

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 69 25
##          1 29 12
##                                           
##                Accuracy : 0.6             
##                  95% CI : (0.5122, 0.6833)
##     No Information Rate : 0.7259          
##     P-Value [Acc > NIR] : 0.9994          
##                                           
##                   Kappa : 0.0275          
##                                           
##  Mcnemar's Test P-Value : 0.6831          
##                                           
##             Sensitivity : 0.7041          
##             Specificity : 0.3243          
##          Pos Pred Value : 0.7340          
##          Neg Pred Value : 0.2927          
##              Prevalence : 0.7259          
##          Detection Rate : 0.5111          
##    Detection Prevalence : 0.6963          
##       Balanced Accuracy : 0.5142          
##                                           
##        'Positive' Class : 0               
## 

## [1] 0.8814815

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Model Comparison:

Lastly, let’s do a model comparison. Overall the top 5 models with the highest accuracies were: Decision Tree (second model), SVM (second and first model), Random Forest (second model), and Gradient Boost Machine (first model).

Decision Tree 2 and SVM 2: Both Decision Tree 2 and SVM 2 achieved similar high accuracies of approximately 91.85%. This implies that these models were quite successful in making accurate predictions for PCOS diagnosis based on the given data and features.

SVM 1:SVM 1 achieved a slightly lower accuracy compared to Decision Tree 2 and SVM 2 but still performed reasonably well at approximately 91.11%. It remains effective in predicting PCOS diagnosis but might be slightly less accurate than the aforementioned models.

Random Forest 2: The Random Forest model achieved an accuracy of around 90.37%, indicating its capability to correctly classify PCOS and non-PCOS cases with slightly less accuracy than SVM 1 and Decision Tree 2.

Gradient Boost Machines 1 and k-Nearest 2: Both Gradient Boost Machines 1 and k-Nearest 2 models obtained accuracies around 88.15%, which, while lower than the previous models, still demonstrate a moderate ability to predict PCOS diagnosis based on the provided features.

Overall, these accuracies suggest that Decision Tree 2, SVM 2, SVM 1, and Random Forest 2 performed relatively better in distinguishing between PCOS and non-PCOS cases in this dataset, while Gradient Boost Machines 1 and k-Nearest 2 showed somewhat lower but still reasonably acceptable prediction accuracies.

Table 5. Model Comparison
Model Accuracy
2 Decision Tree 2 0.9185185
8 SVM 2 0.9185185
7 SVM 1 0.9111111
4 Random Forest 2 0.9037037
5 GBM 1 0.8814815
13 k-NN 2 0.8814815
1 Decision Tree 1 0.8592593
10 Neural Network 1.2 0.7259259
12 k-NN 1 0.6518519
3 Random Forest 1 0.6296296
6 GBM 2 0.6222222
9 Neural Network 1 0.3037037
11 Neural Network 2 0.3037037

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Conclusion:

Initially, I presumed that due to the scarcity of information within the medical domain and the limited availability of datasets for analysis, I held reservations about the potential success in accurately predicting a PCOS diagnosis. Surprisingly, 8 out of the 12 models developed exhibited accuracies surpassing 85%, with only 4 out of the 8 achieving an accuracy exceeding 90%. In comparison to the findings in existing literature utilizing the same Kaggle dataset, the top 5 algorithms I employed showed a slightly lower performance, where these achieved accuracies above 95%. If I had to pick an algorithm to best represent my dataset I’d select SVM 1 as its accuracy was of 91.11%, a unique percentage not replicated by other algorithms. Conversely, the utilization of Neural Networks in this analysis did not yield satisfactory results, deviating from the expectations set by the literature. This outcome might be attributed to the specific package utilized and the parameter settings chosen for the neural network model.

For the future work, I aim to explore datasets such as NICHDash which are not publicly available and feature a slightly larger and more diverse population within the United States. This exploration will facilitate a more comprehensive assessment of PCOS, enabling a deeper understanding of the condition’s nuances and complexities. I’d also futher delve into additional machine learning methodologies highlighted in the literature reviews. Specifically, I intend to gain more hands-on experience with Support Vector Machines, Neural Networks (NN), and K-Nearest Neighbors (K-NN) algorithms. By combining strategies and considering the constraints posed by limited data, researchers | data scientist can potentially improve the predictive capabilities of machine learning models for diagnosing Polycystic Ovarian Syndrome while maintaining robustness and reliability.

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Having subjected the data to various algorithms, I am now able to address the anticipated questions:

  1. Are there commonalities women with and without PCOS have that can be easily dismissed as normal?
  • Some shared patterns identified through analysis included Body Mass Index (BMI), vital signs, biometric measurements, and specific hormone levels, yet within this dataset, insufficient evidence exists to conclusively dismiss these as unrelated to Polycystic Ovary Syndrome (PCOS).
  1. Are there differences for women of different race/ethnic background when it comes to having PCOS? What about women without PCOS?
  • These accuracies don’t directly address racial or ethnic differences as the dataset only involved women from Kerala, India. Further analysis involving feature exploration or specific subgroup analysis using demographic data might unveil associations or variations among different racial/ethnic groups in PCOS prevalence or features.
  1. What is the likelihood of a woman developing PCOS based on her age, ethnicity, and BMI history?
  • The accuracies did not explicitly signify predictions regarding probability due to the lack of diverse ethnic backgrounds in the collected records. Moreover, age and BMI were not influential variables across most of the created models in this dataset. Specific models with feature importance or coefficients might offer insights into how age, ethnicity, and BMI contribute to predicting PCOS likelihood but the data would have to include such diversity.
  1. Can we predict the risk of insulin resistance, diabetes, and cardiovascular disease in women with PCOS based on their medical history, hormone levels, and lifestyle factors?
  • Machine learning models could help predict the risk of insulin resistance, diabetes, and cardiovascular disease in women with PCOS based on available medical history, hormone levels, and lifestyle factors. For this a larger dataset would be needed in order to create specialized modeling to derive precise predictions. Regrettably, my dataset would not be well-suited for conducting such forecasts.
  1. Can we predict the likelihood of successful pregnancy outcomes in women with PCOS based on their age, weight, hormone levels, and treatment history?
  • Similar to the above, machine learning models can potentially predict the likelihood of successful pregnancy outcomes in women with PCOS based on various factors like age, weight, hormone levels, and treatment history. A more expansive dataset, specialized models, or in-depth analyses could offer more intricate predictive insights, exceeding the capabilities of my current dataset.
  1. Can we predict the long-term health outcomes and quality of life of women with PCOS based on their age, lifestyle factors, hormone levels, and treatment history?
  • Machine learning models, when trained with extensive data including age, lifestyle factors, hormone levels, and treatment history, might offer predictive insights into long-term health outcomes and quality of life for women with PCOS. However, these models might need additional feature engineering and specialized analyses to offer accurate predictions. I expect that accurately predicting long-term health outcomes, despite an improved dataset, will remain challenging due to the varied presentation of PCOS in women, which poses ongoing diagnostic challenges.

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References:

  1. Aggarwal, S., & Pandey, K. (2023). Early identification of PCOS with commonly known diseases: Obesity, diabetes, high blood pressure, and heart disease using machine learning techniques. Expert Systems with Applications, 217, 119532. https://doi.org/10.1016/j.eswa.2023.119532

  2. Anda, D., & Iyamah, E. (2022, December). Comparative analysis of artificial intelligence in the diagnosis of … ResearchGate. Retrieved April 1, 2023, from https://www.researchgate.net/publication/366320486_Comparative_Analysis_of_Artificial_Intelligence_in_the_Diagnosis_of_Polycystic_Ovary_Syndrome

  3. Bartlett, E., & Erlich, L. (2015). Part 3: Dealing with Obstacles — Chapter 5: Polycystic Ovary Syndrome (PCOS). In Feed your fertility: Your guide to cultivating a healthy pregnancy with traditional Chinese medicine, real food, and holistic living (pp. 342–349). essay, Fair Winds Press.

  4. Bulsara, J., Patel, P., Soni, A., & Acharya, S. (2021, February 10). A review: Brief insight into polycystic ovarian syndrome. Endocrine and Metabolic Science. Retrieved February 23, 2023, from https://www.sciencedirect.com

  5. Engmann, L., Jin, S., Sun, F., Legro, R. S., Polotsky, A. J., Hansen, K. R., Coutifaris, C., Diamond, M. P., Eisenberg, E., Zhang, H., Santoro, N., & Reproductive Medicine Network (2017). Racial and ethnic differences in the polycystic ovary syndrome metabolic phenotype. American journal of obstetrics and gynecology, 216(5), 493.e1–493.e13. https://doi.org

  6. Goodarzi, Carmina, E., & Azziz, R. (2015). DHEA, DHEAS and PCOS. The Journal of Steroid Biochemistry and Molecular Biology, 145, 213–225. https://doi.org/10.1016/j.jsbmb.2014.06.003

  7. Hassan, Malik & Mirza, Tabasum. (2020). Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome. International Journal of Computer Applications. Volume 175. 10.5120/ijca2020920688.

  8. Khan, M. J., Ullah, A., & Basit, S. (2019). Genetic Basis of Polycystic Ovary Syndrome (PCOS): Current Perspectives. The application of clinical genetics, 12, 249–260. https://doi.org/

  9. Kambale, T., Sawaimul, K. D., & Prakash, S. (2023). A study of hormonal and anthropometric parameters in polycystic ovarian syndrome. Annals of African medicine, 22(1), 112–116. https://doi.org/10.4103/aam.aam_15_22

  10. Kavitha, K., Tangudu, N., Sahu, S. R., Narayana, G. V. L., & Anusha, V. (2023). Detection of PCOS using Machine Learning Algorithms with Grid Search CV Optimization. International Journal of Engineering Trends and Technology, 71(7), 201-208. https://doi.org/10.14445/22315381/IJETT-V71I7P219

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